In [5]:
import pandas as pd
import os
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
import matplotlib as plt
import matplotlib.pyplot as plt
from statsmodels.tsa.arima.model import ARIMA
import yfinance as yf

new_directory = r'C:\Users\DELL\Downloads'  
os.chdir(new_directory)
In [11]:
# Read the CSV file into a pandas DataFrame
data = pd.read_csv('20180101_20180401_bist30.csv', index_col='timestamp', parse_dates=True)
company_1_data = data[data['short_name'] == 'THYAO']


fig, ax =plt.subplots()
company_1_data.plot(ax=ax)
plt.show()

company_1_data= company_1_data['price']

(640, 2)

First, I compared 3 different methods RF, linear regression and ARIMA. I first tried it with data 1, i.e. the first excel file; and looked at THYAO data. I took the 80% as train and the rest as test.For ARIMA, I used the parameters of 30,1,1; which were evaluated in the following codes.

In [69]:
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
import numpy as np
import matplotlib.pyplot as plt

data=company_1_data
target_column = data.values

# Create a DataFrame with lag features
data = pd.DataFrame({'price': target_column})

# Feature engineering: Adding lag features
for i in range(1, 11):
    data[f'lag_{i}'] = data['price'].shift(i)

# Drop rows with NaN values due to lag features
data = data.dropna()



# Split the data into training and testing sets
train_size = int(len(data) * 0.8)
train_data, test_data = data.iloc[:train_size], data.iloc[train_size:]

# Separate features and target variable
X_train, y_train = train_data.drop('price', axis=1), train_data['price']
X_test, y_test = test_data.drop('price', axis=1), test_data['price']

# Create a Random Forest Regressor
rf_model = Pipeline([
    ('scaler', StandardScaler()),  # Standardize the data
    ('random_forest', RandomForestRegressor(n_estimators=100, random_state=42))
])

# Train the model
rf_model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = rf_model.predict(X_test)

# Evaluate the model
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print(f'Root Mean Squared Error: {rmse}')

# Calculate MWAPE
def calculate_mwape(actual, predicted):
    return np.mean(np.abs(actual - predicted) / np.abs(actual))

mwape = calculate_mwape(y_test, y_pred)
print(f'Mean Weighted Absolute Percentage Error (MWAPE): {mwape}')

# Plotting actual vs. predicted prices
plt.figure(figsize=(12, 6))
plt.plot(test_data.index, y_test, label='Actual Prices', color='blue')
plt.plot(test_data.index, y_pred, label='Predicted Prices', color='orange')
plt.title('Random Forest Time Series Forecasting')
plt.xlabel('Timestamp')
plt.ylabel('Price')
plt.legend()
plt.show()

Root Mean Squared Error: 0.15999423055074027
Mean Weighted Absolute Percentage Error (MWAPE): 0.006686265522168634
In [26]:
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt

# Assuming you have a Series named 'series'
# Replace 'your_data.csv' with your actual file or data loading method

# Create a Linear Regression model
linear_model = LinearRegression()

# Train the model
linear_model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = linear_model.predict(X_test)

# Evaluate the model
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print(f'Root Mean Squared Error: {rmse}')

# Calculate MWAPE
def calculate_mwape(actual, predicted):
    return np.mean(np.abs(actual - predicted) / np.abs(actual))

mwape = calculate_mwape(y_test, y_pred)
print(f'Mean Weighted Absolute Percentage Error (MWAPE): {mwape}')

# Plotting actual vs. predicted prices
plt.figure(figsize=(12, 6))
plt.plot(test_data.index, y_test, label='Actual Prices', color='blue')
plt.plot(test_data.index, y_pred, label='Predicted Prices', color='orange')
plt.title('Linear Regression Time Series Forecasting')
plt.xlabel('Timestamp')
plt.ylabel('Price')
plt.legend()
plt.show()

Root Mean Squared Error: 0.12079557962652267
Mean Weighted Absolute Percentage Error (MWAPE): 0.004740389442806533
In [28]:
#ARIMA

##stationary 

company_1_stationary= company_1_data.diff().dropna()
print(company_1_stationary)


from statsmodels.tsa.stattools import adfuller

# Assuming 'data' is your time series data
result = adfuller(company_1_stationary)
print('ADF Statistic:', result[0])
print('p-value:', result[1])
print('Critical Values:', result[4])

#stationary check, it is stationary

timestamp
2018-01-02 10:00:00+03:00    0.22
2018-01-02 11:00:00+03:00    0.04
2018-01-02 12:00:00+03:00    0.00
2018-01-02 13:00:00+03:00    0.01
2018-01-02 14:00:00+03:00   -0.01
                             ... 
2018-03-30 14:00:00+03:00    0.16
2018-03-30 15:00:00+03:00   -0.12
2018-03-30 16:00:00+03:00   -0.15
2018-03-30 17:00:00+03:00    0.03
2018-03-30 18:00:00+03:00   -0.09
Name: price, Length: 639, dtype: float64
ADF Statistic: -16.13966323231865
p-value: 4.690615998630029e-29
Critical Values: {'1%': -3.44065745275905, '5%': -2.8660879520543534, '10%': -2.5691919933016076}
In [29]:
##model=ARIMA(company_1_stationary, order=(p,d,q))
#p (AR - AutoRegressive):  It indicates how many past observations influence the current one.
#d (I - Integrated): the number of differenciating needed to make the data stationary. if the dtaa is already stationary, set it to 0.
#q (MA - Moving Average): It represents the size of the moving average window and indicates the number of lagged forecast errors in the prediction equation. 


model=ARIMA(company_1_data, order=(30,1,1))
results= model.fit()
print(results.summary())

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                  640
Model:                ARIMA(30, 1, 1)   Log Likelihood                 451.757
Date:                Wed, 17 Jan 2024   AIC                           -839.513
Time:                        13:05:44   BIC                           -696.796
Sample:                             0   HQIC                          -784.114
                                - 640                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.2192      2.745     -0.080      0.936      -5.599       5.161
ar.L2          0.0960      0.173      0.555      0.579      -0.243       0.435
ar.L3         -0.0230      0.220     -0.104      0.917      -0.455       0.409
ar.L4         -0.0125      0.125     -0.100      0.920      -0.258       0.233
ar.L5          0.0390      0.042      0.933      0.351      -0.043       0.121
ar.L6          0.0609      0.110      0.555      0.579      -0.154       0.276
ar.L7          0.0137      0.142      0.096      0.923      -0.266       0.293
ar.L8         -0.0055      0.043     -0.126      0.899      -0.090       0.079
ar.L9         -0.0148      0.049     -0.304      0.761      -0.110       0.081
ar.L10         0.0093      0.058      0.161      0.872      -0.104       0.122
ar.L11        -0.0124      0.054     -0.227      0.820      -0.119       0.094
ar.L12        -0.0696      0.060     -1.153      0.249      -0.188       0.049
ar.L13        -0.0507      0.198     -0.256      0.798      -0.439       0.338
ar.L14         0.0134      0.106      0.127      0.899      -0.194       0.221
ar.L15         0.0225      0.078      0.289      0.773      -0.130       0.176
ar.L16         0.0193      0.067      0.289      0.773      -0.112       0.151
ar.L17         0.0169      0.061      0.278      0.781      -0.102       0.136
ar.L18        -0.0983      0.057     -1.710      0.087      -0.211       0.014
ar.L19         0.0504      0.286      0.176      0.860      -0.511       0.612
ar.L20        -0.0092      0.226     -0.041      0.968      -0.452       0.433
ar.L21        -0.0941      0.095     -0.988      0.323      -0.281       0.093
ar.L22        -0.0420      0.240     -0.175      0.861      -0.513       0.429
ar.L23         0.0550      0.069      0.802      0.422      -0.079       0.190
ar.L24        -0.0169      0.174     -0.097      0.923      -0.358       0.324
ar.L25        -0.0233      0.101     -0.231      0.818      -0.221       0.175
ar.L26        -0.0123      0.063     -0.196      0.845      -0.135       0.111
ar.L27         0.0167      0.059      0.285      0.776      -0.098       0.132
ar.L28        -0.0054      0.066     -0.082      0.934      -0.134       0.123
ar.L29         0.1052      0.049      2.150      0.032       0.009       0.201
ar.L30         0.0455      0.292      0.156      0.876      -0.526       0.617
ma.L1          0.2791      2.751      0.101      0.919      -5.113       5.671
sigma2         0.0142      0.001     22.371      0.000       0.013       0.015
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):               200.26
Prob(Q):                              0.99   Prob(JB):                         0.00
Heteroskedasticity (H):               1.60   Skew:                             0.12
Prob(H) (two-sided):                  0.00   Kurtosis:                         5.73
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
In [33]:
forecast=results.get_prediction(start=-128)
mean_forecast=forecast.predicted_mean
confidence_intervals=forecast.conf_int()
print(confidence_intervals)
lower_limits = confidence_intervals.loc[:,'lower price']
upper_limits = confidence_intervals.loc[:,'upper price']

                           lower price  upper price
timestamp                                          
2018-03-14 11:00:00+03:00    17.924113    18.391432
2018-03-14 12:00:00+03:00    17.849370    18.316688
2018-03-14 13:00:00+03:00    18.122321    18.589640
2018-03-14 14:00:00+03:00    18.093302    18.560621
2018-03-14 15:00:00+03:00    18.319440    18.786759
...                                ...          ...
2018-03-30 14:00:00+03:00    19.021689    19.489007
2018-03-30 15:00:00+03:00    19.186758    19.654077
2018-03-30 16:00:00+03:00    19.096760    19.564079
2018-03-30 17:00:00+03:00    18.923420    19.390738
2018-03-30 18:00:00+03:00    18.923556    19.390875

[128 rows x 2 columns]
In [38]:
time= company_1_data.index

plt.figure()

plt.plot(time[512:640], mean_forecast.values, color='red', label='forecast')

plt.fill_between(time[512:640], lower_limits, upper_limits, color='pink')

plt.plot(time[512:640], company_1_data[512:640], label='Actual Data')  # Replace with your actual data

plt.show



# Evaluate the model
rmse = np.sqrt(mean_squared_error(company_1_data[512:640], mean_forecast.values ))
print(f'Root Mean Squared Error: {rmse}')

# Calculate MWAPE
def calculate_mwape(actual, predicted):
    return np.mean(np.abs(actual - predicted) / np.abs(actual))

mwape = calculate_mwape(company_1_data[512:640], mean_forecast.values )
print(f'Mean Weighted Absolute Percentage Error (MWAPE): {mwape}')

Root Mean Squared Error: 0.11624825010362062
Mean Weighted Absolute Percentage Error (MWAPE): 0.00452682322171308

Then, i tried with another company, ARCLK and another time interval. Both the previous and this comparison gave the result of ARIMA method is working the best.

In [68]:
data16= pd.read_csv('20210927_20211226_bist30.csv', index_col='timestamp', parse_dates=True)
companydata = data16[data16['short_name'] == 'ARCLK']
companydata= companydata['price']

data = companydata
target_column = data.values

# Create a DataFrame with lag features
data = pd.DataFrame({'price': target_column})

# Feature engineering: Adding lag features
for i in range(1, 11):
    data[f'lag_{i}'] = data['price'].shift(i)

# Drop rows with NaN values due to lag features
data = data.dropna()



# Split the data into training and testing sets
train_size = int(len(data) * 0.8)
train_data, test_data = data.iloc[:train_size], data.iloc[train_size:]

# Separate features and target variable
X_train, y_train = train_data.drop('price', axis=1), train_data['price']
X_test, y_test = test_data.drop('price', axis=1), test_data['price']

#RF

# Create a Random Forest Regressor
rf_model = Pipeline([
    ('scaler', StandardScaler()),  # Standardize the data
    ('random_forest', RandomForestRegressor(n_estimators=100, random_state=42))
])

# Train the model
rf_model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = rf_model.predict(X_test)

# Evaluate the model
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print(f'RF:Root Mean Squared Error: {rmse}')

# Calculate MWAPE
def calculate_mwape(actual, predicted):
    return np.mean(np.abs(actual - predicted) / np.abs(actual))

mwape = calculate_mwape(y_test, y_pred)
print(f'RF:Mean Weighted Absolute Percentage Error (MWAPE): {mwape}')

# Plotting actual vs. predicted prices
plt.figure(figsize=(12, 6))
plt.plot(test_data.index, y_test, label='Actual Prices', color='blue')
plt.plot(test_data.index, y_pred, label='Predicted Prices', color='orange')
plt.title('Random Forest Time Series Forecasting')
plt.xlabel('Timestamp')
plt.ylabel('Price')
plt.legend()
plt.show()


#Linear Regression
# Create a Linear Regression model
linear_model = LinearRegression()

# Train the model
linear_model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = linear_model.predict(X_test)

# Evaluate the model
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print(f'LR:Root Mean Squared Error: {rmse}')

# Calculate MWAPE
def calculate_mwape(actual, predicted):
    return np.mean(np.abs(actual - predicted) / np.abs(actual))

mwape = calculate_mwape(y_test, y_pred)
print(f'LR:Mean Weighted Absolute Percentage Error (MWAPE): {mwape}')

# Plotting actual vs. predicted prices
plt.figure(figsize=(12, 6))
plt.plot(test_data.index, y_test, label='Actual Prices', color='blue')
plt.plot(test_data.index, y_pred, label='Predicted Prices', color='orange')
plt.title('Linear Regression Time Series Forecasting')
plt.xlabel('Timestamp')
plt.ylabel('Price')
plt.legend()
plt.show()

##model=ARIMA(company_1_stationary, order=(p,d,q))
#p (AR - AutoRegressive):  It indicates how many past observations influence the current one.
#d (I - Integrated): the number of differenciating needed to make the data stationary. if the dtaa is already stationary, set it to 0.
#q (MA - Moving Average): It represents the size of the moving average window and indicates the number of lagged forecast errors in the prediction equation. 


model=ARIMA(companydata, order=(30,1,1))
results= model.fit()

time= data.index
forecast=results.get_prediction(start=-128)
mean_forecast=forecast.predicted_mean
confidence_intervals=forecast.conf_int()

lower_limits = confidence_intervals.loc[:,'lower price']
upper_limits = confidence_intervals.loc[:,'upper price']

plt.figure()

plt.plot(time[496:640], mean_forecast.values, color='red', label='forecast')

plt.fill_between(time[496:640], lower_limits, upper_limits, color='pink')

plt.plot(time[496:640], companydata[506:640], label='Actual Data')  # Replace with your actual data

plt.show



# Evaluate the model
rmse = np.sqrt(mean_squared_error(companydata[506:640], mean_forecast.values ))
print(f'ARIMA:Root Mean Squared Error: {rmse}')

# Calculate MWAPE
def calculate_mwape(actual, predicted):
    return np.mean(np.abs(actual - predicted) / np.abs(actual))

mwape = calculate_mwape(companydata[506:640], mean_forecast.values )
print(f'ARIMA:Mean Weighted Absolute Percentage Error (MWAPE): {mwape}')

RF:Root Mean Squared Error: 2.8936798721471537
RF:Mean Weighted Absolute Percentage Error (MWAPE): 0.041290602494520025

LR:Root Mean Squared Error: 1.0828321818721736
LR:Mean Weighted Absolute Percentage Error (MWAPE): 0.013146473734283818

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

ARIMA:Root Mean Squared Error: 0.9260238039211902
ARIMA:Mean Weighted Absolute Percentage Error (MWAPE): 0.01197928330313338

Following code is only for visualization purposes

In [86]:
import pandas as pd
import matplotlib.pyplot as plt


forecast = results.get_prediction(start=len(company_1_data), end=len(company_1_data) + 24)
mean_forecast = forecast.predicted_mean
confidence_intervals = forecast.conf_int()
lower_limits = confidence_intervals.loc[:, 'lower price']
upper_limits = confidence_intervals.loc[:, 'upper price']


# Create numerical index for actual data
num_indices = range(1, len(company_1_data) + 1)
company_1_data.index = num_indices  # Reindexing with numerical indices

# Create numerical index for forecasted data (641 to 665)
num_new_indices = range(len(company_1_data) + 1, len(company_1_data) + len(mean_forecast) + 1)

plt.figure()

# Plotting the actual data with numerical indices
plt.plot(num_indices, company_1_data, label='Actual Data', color='blue')

# Plotting the forecasted values with new numerical indices
plt.plot(num_new_indices, mean_forecast.values, color='red', label='Forecast')

# Fill between the confidence intervals
plt.fill_between(num_new_indices, lower_limits, upper_limits, color='pink')

plt.xlabel('Index')  # Update with appropriate label
plt.ylabel('Price')  # Update with appropriate label
plt.title('Actual Data and Forecast')
plt.legend()
plt.show()
In [2]:
data1= pd.read_csv('20180101_20180401_bist30.csv', index_col='timestamp', parse_dates=True)
data2= pd.read_csv('20180402_20180701_bist30.csv', index_col='timestamp', parse_dates=True)
data3= pd.read_csv('20180702_20180930_bist30.csv', index_col='timestamp', parse_dates=True)
data4= pd.read_csv('20181001_20181230_bist30.csv', index_col='timestamp', parse_dates=True)
data5= pd.read_csv('20181231_20190331_bist30.csv', index_col='timestamp', parse_dates=True)
data6= pd.read_csv('20190401_20190630_bist30.csv', index_col='timestamp', parse_dates=True)
data7= pd.read_csv('20190701_20190929_bist30.csv', index_col='timestamp', parse_dates=True)
data8= pd.read_csv('20190930_20191229_bist30.csv', index_col='timestamp', parse_dates=True)
data9= pd.read_csv('20191230_20200329_bist30.csv', index_col='timestamp', parse_dates=True)
data10= pd.read_csv('20200330_20200628_bist30.csv', index_col='timestamp', parse_dates=True)
data11= pd.read_csv('20200629_20200927_bist30.csv', index_col='timestamp', parse_dates=True)
data12= pd.read_csv('20200928_20201227_bist30.csv', index_col='timestamp', parse_dates=True)
data13= pd.read_csv('20201228_20210328_bist30.csv', index_col='timestamp', parse_dates=True)
data14= pd.read_csv('20210329_20210627_bist30.csv', index_col='timestamp', parse_dates=True)
data15= pd.read_csv('20210628_20210926_bist30.csv', index_col='timestamp', parse_dates=True)
data16= pd.read_csv('20210927_20211226_bist30.csv', index_col='timestamp', parse_dates=True)
data17= pd.read_csv('20211227_20220327_bist30.csv', index_col='timestamp', parse_dates=True)
data18= pd.read_csv('20220328_20220626_bist30.csv', index_col='timestamp', parse_dates=True)
data19= pd.read_csv('20220627_20220925_bist30.csv', index_col='timestamp', parse_dates=True)
data20= pd.read_csv('20220926_20221225_bist30.csv', index_col='timestamp', parse_dates=True)
data21= pd.read_csv('20221226_20230326_bist30.csv', index_col='timestamp', parse_dates=True)
data22= pd.read_csv('20230327_20230625_bist30.csv', index_col='timestamp', parse_dates=True)
data23= pd.read_csv('20230626_20230924_bist30.csv', index_col='timestamp', parse_dates=True)
data24= pd.read_csv('20230925_20231224_bist30.csv', index_col='timestamp', parse_dates=True)

Here, I have uploaded all the data and did cross validation, i.e. i took different train and test data sets. Then i found that 30,1,1 parameters will work the best considering all cases.

In [492]:
#15,17,18,19, 20, 21, 22, 23, 24
traindata=pd.concat([data1, data2, data3,data4, data5, data6, data7, data8, data9, data10, data11,data12, data13, data14, data15,data16,data17,data18,data19,data20,data21,data22,data23])
testdata=data24

# Display combined_data
print(traindata.head(10))

company_1_data = traindata[traindata['short_name'] == 'THYAO']
company_1_data= company_1_data.drop(columns=['short_name'])
company_1_datat=  testdata[testdata['short_name'] == 'THYAO']
company_1_datat= company_1_datat.drop(columns=['short_name'])
company_1_datatest= company_1_datat

                           price short_name
timestamp                                  
2018-01-02 09:00:00+03:00  15.79      THYAO
2018-01-02 10:00:00+03:00  16.01      THYAO
2018-01-02 11:00:00+03:00  16.05      THYAO
2018-01-02 12:00:00+03:00  16.05      THYAO
2018-01-02 13:00:00+03:00  16.06      THYAO
2018-01-02 14:00:00+03:00  16.05      THYAO
2018-01-02 15:00:00+03:00  16.11      THYAO
2018-01-02 16:00:00+03:00  16.13      THYAO
2018-01-02 17:00:00+03:00  16.08      THYAO
2018-01-02 18:00:00+03:00  16.08      THYAO
In [463]:
from statsmodels.tsa.stattools import adfuller
##stationary

company_1_stationary= company_1_data.diff().dropna()
print(company_1_stationary)

# Assuming 'data' is your time series data
resultxx = adfuller(company_1_stationary)
print('ADF Statistic:', resultxx[0])
print('p-value:', resultxx[1])
print('Critical Values:', resultxx[4])

#stationary check, it is stationary(ADF lower than critical values and p-value close to 0)

                           price
timestamp                       
2018-01-02 10:00:00+03:00   0.22
2018-01-02 11:00:00+03:00   0.04
2018-01-02 12:00:00+03:00   0.00
2018-01-02 13:00:00+03:00   0.01
2018-01-02 14:00:00+03:00  -0.01
...                          ...
2023-10-20 14:00:00+03:00  -0.20
2023-10-20 15:00:00+03:00  -0.20
2023-10-20 16:00:00+03:00  -1.50
2023-10-20 17:00:00+03:00   0.20
2023-10-20 18:00:00+03:00  -0.30

[14426 rows x 1 columns]
ADF Statistic: -16.616757692900425
p-value: 1.7119371132762855e-29
Critical Values: {'1%': -3.4308046727350603, '5%': -2.8617409450488616, '10%': -2.5668769583088533}
In [42]:
##model=ARIMA(company_1_stationary, order=(p,d,q))
#p (AR - AutoRegressive):  It indicates how many past observations influence the current one.
#d (I - Integrated): the number of differenciating needed to make the data stationary. if the dtaa is already stationary, set it to 0.
#q (MA - Moving Average): It represents the size of the moving average window and indicates the number of lagged forecast errors in the prediction equation. 


model=ARIMA(company_1_data, order=(30,1,1))
results= model.fit()
print(results.summary())

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                  640
Model:                ARIMA(30, 1, 1)   Log Likelihood                 451.757
Date:                Wed, 17 Jan 2024   AIC                           -839.513
Time:                        13:22:19   BIC                           -696.796
Sample:                             0   HQIC                          -784.114
                                - 640                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.2192      2.745     -0.080      0.936      -5.599       5.161
ar.L2          0.0960      0.173      0.555      0.579      -0.243       0.435
ar.L3         -0.0230      0.220     -0.104      0.917      -0.455       0.409
ar.L4         -0.0125      0.125     -0.100      0.920      -0.258       0.233
ar.L5          0.0390      0.042      0.933      0.351      -0.043       0.121
ar.L6          0.0609      0.110      0.555      0.579      -0.154       0.276
ar.L7          0.0137      0.142      0.096      0.923      -0.266       0.293
ar.L8         -0.0055      0.043     -0.126      0.899      -0.090       0.079
ar.L9         -0.0148      0.049     -0.304      0.761      -0.110       0.081
ar.L10         0.0093      0.058      0.161      0.872      -0.104       0.122
ar.L11        -0.0124      0.054     -0.227      0.820      -0.119       0.094
ar.L12        -0.0696      0.060     -1.153      0.249      -0.188       0.049
ar.L13        -0.0507      0.198     -0.256      0.798      -0.439       0.338
ar.L14         0.0134      0.106      0.127      0.899      -0.194       0.221
ar.L15         0.0225      0.078      0.289      0.773      -0.130       0.176
ar.L16         0.0193      0.067      0.289      0.773      -0.112       0.151
ar.L17         0.0169      0.061      0.278      0.781      -0.102       0.136
ar.L18        -0.0983      0.057     -1.710      0.087      -0.211       0.014
ar.L19         0.0504      0.286      0.176      0.860      -0.511       0.612
ar.L20        -0.0092      0.226     -0.041      0.968      -0.452       0.433
ar.L21        -0.0941      0.095     -0.988      0.323      -0.281       0.093
ar.L22        -0.0420      0.240     -0.175      0.861      -0.513       0.429
ar.L23         0.0550      0.069      0.802      0.422      -0.079       0.190
ar.L24        -0.0169      0.174     -0.097      0.923      -0.358       0.324
ar.L25        -0.0233      0.101     -0.231      0.818      -0.221       0.175
ar.L26        -0.0123      0.063     -0.196      0.845      -0.135       0.111
ar.L27         0.0167      0.059      0.285      0.776      -0.098       0.132
ar.L28        -0.0054      0.066     -0.082      0.934      -0.134       0.123
ar.L29         0.1052      0.049      2.150      0.032       0.009       0.201
ar.L30         0.0455      0.292      0.156      0.876      -0.526       0.617
ma.L1          0.2791      2.751      0.101      0.919      -5.113       5.671
sigma2         0.0142      0.001     22.371      0.000       0.013       0.015
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):               200.26
Prob(Q):                              0.99   Prob(JB):                         0.00
Heteroskedasticity (H):               1.60   Skew:                             0.12
Prob(H) (two-sided):                  0.00   Kurtosis:                         5.73
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
In [43]:
forecast = results.get_prediction(start=len(company_1_data), end=len(company_1_data) + 9)
mean_forecast = forecast.predicted_mean
confidence_intervals = forecast.conf_int()
lower_limits = confidence_intervals.loc[:, 'lower price']
upper_limits = confidence_intervals.loc[:, 'upper price']

print(mean_forecast)
print(confidence_intervals)

640    19.106957
641    19.084017
642    19.046349
643    19.003089
644    19.002918
645    19.018835
646    18.983433
647    18.955846
648    18.953437
649    18.959358
Name: predicted_mean, dtype: float64
     lower price  upper price
640    18.873297    19.340616
641    18.743530    19.424504
642    18.613653    19.479044
643    18.498936    19.507242
644    18.436056    19.569780
645    18.392345    19.645325
646    18.297282    19.669584
647    18.213774    19.697919
648    18.159284    19.747589
649    18.117652    19.801063

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
In [509]:
plt.figure()

# Plotting the forecasted values and confidence intervals
plt.plot(mean_forecast.values, color='red', label='Forecast')
plt.fill_between(range(len(mean_forecast)), lower_limits, upper_limits, color='pink', alpha=0.3)

# Plotting the first 25 points of company_1_data2 as 'Actual Data'
plt.plot(range(10), company_1_datatest.iloc[:10], label='Actual Data', marker='o')  # Adjust the slicing as needed

plt.xlabel('Index')  # Update with appropriate label
plt.ylabel('Values')  # Update with appropriate label
plt.title('Comparison between Forecast and Actual Data')
plt.legend()
plt.show()
In [510]:
from sklearn.metrics import mean_squared_error
import numpy as np

# Assuming 'actual_data' contains your actual data and 'mean_forecast' contains the mean forecast
# Convert 'actual_data' and 'mean_forecast' to numpy arrays if they are not already
actual_values = np.array(company_1_datatest.iloc[:10])
forecast_values = np.array(mean_forecast)

# Calculate RMSE
rmse = np.sqrt(mean_squared_error(actual_values, forecast_values))
print(f"Root Mean Squared Error (RMSE): {rmse}")

#data4 - first 10

#20,1,10 : Root Mean Squared Error (RMSE): 0.21956257305143428
#30,1,10 :Root Mean Squared Error (RMSE): 0.2326478310491985
#2,1,1: Root Mean Squared Error (RMSE): 0.22378102154677745
#20,1,1: Root Mean Squared Error (RMSE): 0.2266429185264849
#20,1,20: Root Mean Squared Error (RMSE): 0.23786748201306226
#10,1,20:Root Mean Squared Error (RMSE): 0.24234514016904027
#10,1,10: Root Mean Squared Error (RMSE): 0.21992749712260598
#5,1,5 : Root Mean Squared Error (RMSE): 0.21384506077829027
#5,1,15 : Root Mean Squared Error (RMSE): 0.2193320424305745

#data 4- 100-110
#5,1,5 : Root Mean Squared Error (RMSE):  0.3823246691112294
#20,1,10 : Root Mean Squared Error (RMSE): 0.41061901519509086
#2,1,1: Root Mean Squared Error (RMSE): 0.3695443767607099
#2,1,4: Root Mean Squared Error (RMSE): 0.3599197914194394
#30,1,30:Root Mean Squared Error (RMSE): 0.4199183885303645

#data 4- 200-210
#2,1,4: Root Mean Squared Error (RMSE):0.6609514819410949
#20,1,10: Root Mean Squared Error (RMSE): 0.7099041139672777
#5,1,5: Root Mean Squared Error (RMSE): 0.6735294955328522
#2,1,1: Root Mean Squared Error (RMSE): 0.6438404448345101
#30,1,30: Root Mean Squared Error (RMSE): 0.6873118233406335
#50,1,50: Root Mean Squared Error (RMSE): 0.6808970326784374

#data 4 300-310
#2,1,1: Root Mean Squared Error (RMSE): 0.4962259971060228
#5,1,5: Root Mean Squared Error (RMSE): 0.49304198303767993
#2,1,4: Root Mean Squared Error (RMSE): 0.4990993617925751
#20,1,10: Root Mean Squared Error (RMSE): 0.4564365772463498
#30,1,30: Root Mean Squared Error (RMSE): 0.41448340116426996
#50,1,50: Root Mean Squared Error (RMSE): 0.4475610105688453

#data 4 400. data
#30,1,30: Root Mean Squared Error (RMSE): 0.058528847093128014
#2,1,1: Root Mean Squared Error (RMSE): 0.09914657230999979

#data 4 500.data
#2,1,1: Root Mean Squared Error (RMSE): 0.09015620713008844
#30,1,30: Root Mean Squared Error (RMSE): 0.10184539368529033

#data 9 first 10
#30,1,30:  Root Mean Squared Error (RMSE): 0.3049194254793206
#2,1,1: Root Mean Squared Error (RMSE): 0.3002653180126832

#data 13 first 10
#2,1,1: Root Mean Squared Error (RMSE): 0.07527111565529547
#30,1,30: Root Mean Squared Error (RMSE): 0.08361974869901259

#data 20 first 10
#2,1,1 : Root Mean Squared Error (RMSE): 1.504191996762454
#30,1,30: Root Mean Squared Error (RMSE): 1.2950045320502124
#mean for comparison: 69.46, normalised (1.29)= 0.0185

#data 21 first 10
#2,1,1: Root Mean Squared Error (RMSE): 2.6380470886728817,,, Normalised=0.018
#30,1,30: Root Mean Squared Error (RMSE): 1.9196676855508452



#data 24 first 10
#2,1,1: Root Mean Squared Error (RMSE): 4.449751175853234
#30,1,30: Root Mean Squared Error (RMSE): 5.269123138874844
#5,1,5: RMSE: 4.5 ama trendi yakaladı
#30,1,1: Root Mean Squared Error (RMSE): 4.766827733192989
#30,1,5: Root Mean Squared Error (RMSE): 4.877291772333265


#data24 100.data
#5,1,5: Root Mean Squared Error (RMSE): 8.201136821838128, but predicts opposite trend 
#2,1,1:Root Mean Squared Error (RMSE): 8.102811835768671
#30,1,1:Root Mean Squared Error (RMSE): 7.6783937147890615
#30,1,5: Root Mean Squared Error (RMSE): 7.951948663947562
#70,1,1: Root Mean Squared Error (RMSE): 8.923404637184442

#data24 200. data
#30,1,1:Root Mean Squared Error (RMSE): 5.245856233985591
#2,1,1: Root Mean Squared Error (RMSE): 5.155837010840061



#CHOOSING 30,1,1 by considering all

Root Mean Squared Error (RMSE): 4.766827733192989

Then, I put these codes into a function and applied to all companies to see how it worked. Then, I added yahoo finance data in a function (forecastplusyahoo) and tried all companies as well. I have adjusted some companies parameters, if the model is underpredicting I have adjusted to model to 30,1,3 parameters. You can search for "next" in the file to skip the results.

In [547]:
def arima_forecast(name, p, d, q):
 
    # Filter train and test data for a specific short_name
    company_2_data = traindata[traindata['short_name'] == name]
    company_2_data = company_2_data.drop(columns=['short_name'])
    print(company_2_data.tail(30))
    
    company_2_datat = testdata[testdata['short_name'] == name]
    company_2_datat = company_2_datat.drop(columns=['short_name'])
    company_2_datatest = company_2_datat
    
    mean_first_10 = company_2_datatest.head(10).mean()
    print("Mean of the first 10 values:", mean_first_10)
    
    print(company_2_datatest.head(10))
    
    from statsmodels.tsa.stattools import adfuller
    # Stationary check
    company_2_stationary = company_2_data.diff().dropna()
    print(company_2_stationary)

    resultxx = adfuller(company_2_stationary)
    print('ADF Statistic:', resultxx[0])
    print('p-value:', resultxx[1])
    print('Critical Values:', resultxx[4])
    
    # ARIMA modeling
    model = ARIMA(company_2_data, order=(p, d, q))
    results = model.fit()
    print(results.summary())
    
    forecast = results.get_prediction(start=len(company_2_data), end=len(company_2_data) + 9)
    mean_forecast = forecast.predicted_mean
    confidence_intervals = forecast.conf_int()
    lower_limits = confidence_intervals.loc[:, 'lower price']
    upper_limits = confidence_intervals.loc[:, 'upper price']
    
    print(mean_forecast)
    print(confidence_intervals)
    
    # Plotting the forecasted values and actual data
    plt.figure()
    plt.plot(mean_forecast.values, color='red', label='Forecast')
    plt.fill_between(range(len(mean_forecast)), lower_limits, upper_limits, color='pink', alpha=0.3)
    plt.plot(range(10), company_2_datatest.iloc[:10], label='Actual Data', marker='o')
    
    plt.xlabel('Index')
    plt.ylabel('Values')
    plt.title('Comparison between Forecast and Actual Data')
    plt.legend()
    plt.show()
    
    # Calculating RMSE
    from sklearn.metrics import mean_squared_error
    import numpy as np

    actual_values = np.array(company_2_datatest.iloc[:10])
    forecast_values = np.array(mean_forecast)

    rmse = np.sqrt(mean_squared_error(actual_values, forecast_values))
    print(f"Root Mean Squared Error (RMSE): {rmse}")
In [681]:
arima_forecast('THYAO', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  226.2
2023-09-20 10:00:00+03:00  225.4
2023-09-20 11:00:00+03:00  224.4
2023-09-20 12:00:00+03:00  224.1
2023-09-20 13:00:00+03:00  224.4
2023-09-20 14:00:00+03:00  225.4
2023-09-20 15:00:00+03:00  225.3
2023-09-20 16:00:00+03:00  224.8
2023-09-20 17:00:00+03:00  223.6
2023-09-20 18:00:00+03:00  223.3
2023-09-21 09:00:00+03:00  222.0
2023-09-21 10:00:00+03:00  218.9
2023-09-21 11:00:00+03:00  220.5
2023-09-21 12:00:00+03:00  221.3
2023-09-21 13:00:00+03:00  221.4
2023-09-21 14:00:00+03:00  227.9
2023-09-21 15:00:00+03:00  226.6
2023-09-21 16:00:00+03:00  228.6
2023-09-21 17:00:00+03:00  232.9
2023-09-21 18:00:00+03:00  232.6
2023-09-22 09:00:00+03:00  233.0
2023-09-22 10:00:00+03:00  231.1
2023-09-22 11:00:00+03:00  232.2
2023-09-22 12:00:00+03:00  231.7
2023-09-22 13:00:00+03:00  230.7
2023-09-22 14:00:00+03:00  228.8
2023-09-22 15:00:00+03:00  227.9
2023-09-22 16:00:00+03:00  228.2
2023-09-22 17:00:00+03:00  226.8
2023-09-22 18:00:00+03:00  226.1
Mean of the first 10 values: price    229.86
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  227.0
2023-09-25 10:00:00+03:00  226.9
2023-09-25 11:00:00+03:00  228.5
2023-09-25 12:00:00+03:00  229.7
2023-09-25 13:00:00+03:00  231.0
2023-09-25 14:00:00+03:00  229.9
2023-09-25 15:00:00+03:00  229.0
2023-09-25 16:00:00+03:00  231.3
2023-09-25 17:00:00+03:00  232.8
2023-09-25 18:00:00+03:00  232.5
                           price
timestamp                       
2018-01-02 10:00:00+03:00   0.22
2018-01-02 11:00:00+03:00   0.04
2018-01-02 12:00:00+03:00   0.00
2018-01-02 13:00:00+03:00   0.01
2018-01-02 14:00:00+03:00  -0.01
...                          ...
2023-09-22 14:00:00+03:00  -1.90
2023-09-22 15:00:00+03:00  -0.90
2023-09-22 16:00:00+03:00   0.30
2023-09-22 17:00:00+03:00  -1.40
2023-09-22 18:00:00+03:00  -0.70

[14226 rows x 1 columns]
ADF Statistic: -16.710760166698364
p-value: 1.4352632020639404e-29
Critical Values: {'1%': -3.43081108450881, '5%': -2.861743778546585, '10%': -2.5668784665510107}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood              -12824.162
Date:                Sun, 24 Dec 2023   AIC                          25712.323
Time:                        18:25:08   BIC                          25954.334
Sample:                             0   HQIC                         25792.828
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.8750      0.014    -60.479      0.000      -0.903      -0.847
ar.L2          0.0401      0.004     10.080      0.000       0.032       0.048
ar.L3          0.0235      0.004      5.224      0.000       0.015       0.032
ar.L4          0.0255      0.004      5.782      0.000       0.017       0.034
ar.L5          0.0150      0.005      3.191      0.001       0.006       0.024
ar.L6          0.0575      0.005     12.036      0.000       0.048       0.067
ar.L7          0.0594      0.005     12.848      0.000       0.050       0.068
ar.L8         -0.0042      0.005     -0.851      0.395      -0.014       0.005
ar.L9          0.0021      0.005      0.426      0.670      -0.008       0.012
ar.L10        -0.0319      0.004     -8.095      0.000      -0.040      -0.024
ar.L11        -0.0525      0.004    -12.908      0.000      -0.060      -0.044
ar.L12        -0.0518      0.004    -11.713      0.000      -0.060      -0.043
ar.L13        -0.0392      0.005     -8.486      0.000      -0.048      -0.030
ar.L14         0.0402      0.005      8.474      0.000       0.031       0.050
ar.L15         0.0285      0.005      5.308      0.000       0.018       0.039
ar.L16        -0.0256      0.004     -5.847      0.000      -0.034      -0.017
ar.L17         0.0084      0.005      1.625      0.104      -0.002       0.019
ar.L18         0.0223      0.005      4.118      0.000       0.012       0.033
ar.L19        -0.0242      0.005     -4.644      0.000      -0.034      -0.014
ar.L20        -0.0406      0.004     -9.692      0.000      -0.049      -0.032
ar.L21         0.0040      0.005      0.825      0.409      -0.005       0.013
ar.L22         0.0137      0.005      2.676      0.007       0.004       0.024
ar.L23        -0.0288      0.005     -5.817      0.000      -0.039      -0.019
ar.L24        -0.0351      0.005     -7.376      0.000      -0.044      -0.026
ar.L25         0.0217      0.004      4.820      0.000       0.013       0.030
ar.L26         0.0515      0.005     10.527      0.000       0.042       0.061
ar.L27         0.0455      0.004     11.246      0.000       0.038       0.053
ar.L28         0.0267      0.005      5.122      0.000       0.016       0.037
ar.L29         0.0324      0.005      6.723      0.000       0.023       0.042
ar.L30         0.0464      0.003     13.977      0.000       0.040       0.053
ma.L1          0.9225      0.014     64.488      0.000       0.894       0.951
sigma2         0.3552      0.001    413.839      0.000       0.354       0.357
===================================================================================
Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):           3634251.92
Prob(Q):                              0.93   Prob(JB):                         0.00
Heteroskedasticity (H):              73.28   Skew:                             2.69
Prob(H) (two-sided):                  0.00   Kurtosis:                        81.12
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    225.829578
14228    225.733142
14229    225.891536
14230    225.850625
14231    225.722649
14232    225.596491
14233    225.677413
14234    225.423627
14235    225.195873
14236    225.212440
Name: predicted_mean, dtype: float64
       lower price  upper price
14227   224.661421   226.997735
14228   224.041448   227.424836
14229   223.804681   227.978392
14230   223.416471   228.284779
14231   222.983153   228.462144
14232   222.575362   228.617620
14233   222.377381   228.977444
14234   221.856409   228.990845
14235   221.387883   229.003863
14236   221.169361   229.255519

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 4.766827733192989
In [682]:
arima_forecast('AKBNK', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  29.06
2023-09-20 10:00:00+03:00  29.60
2023-09-20 11:00:00+03:00  29.84
2023-09-20 12:00:00+03:00  29.86
2023-09-20 13:00:00+03:00  29.94
2023-09-20 14:00:00+03:00  29.94
2023-09-20 15:00:00+03:00  30.00
2023-09-20 16:00:00+03:00  29.86
2023-09-20 17:00:00+03:00  29.56
2023-09-20 18:00:00+03:00  29.60
2023-09-21 09:00:00+03:00  29.94
2023-09-21 10:00:00+03:00  30.52
2023-09-21 11:00:00+03:00  30.50
2023-09-21 12:00:00+03:00  30.44
2023-09-21 13:00:00+03:00  30.68
2023-09-21 14:00:00+03:00  30.18
2023-09-21 15:00:00+03:00  30.16
2023-09-21 16:00:00+03:00  30.66
2023-09-21 17:00:00+03:00  31.24
2023-09-21 18:00:00+03:00  31.20
2023-09-22 09:00:00+03:00  31.20
2023-09-22 10:00:00+03:00  31.08
2023-09-22 11:00:00+03:00  31.26
2023-09-22 12:00:00+03:00  31.20
2023-09-22 13:00:00+03:00  31.44
2023-09-22 14:00:00+03:00  31.08
2023-09-22 15:00:00+03:00  30.90
2023-09-22 16:00:00+03:00  31.14
2023-09-22 17:00:00+03:00  30.84
2023-09-22 18:00:00+03:00  30.80
Mean of the first 10 values: price    31.734
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  30.94
2023-09-25 10:00:00+03:00  31.20
2023-09-25 11:00:00+03:00  31.32
2023-09-25 12:00:00+03:00  31.74
2023-09-25 13:00:00+03:00  31.98
2023-09-25 14:00:00+03:00  31.58
2023-09-25 15:00:00+03:00  31.78
2023-09-25 16:00:00+03:00  32.30
2023-09-25 17:00:00+03:00  32.28
2023-09-25 18:00:00+03:00  32.22
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.1127
2018-01-02 11:00:00+03:00  0.0352
2018-01-02 12:00:00+03:00 -0.0140
2018-01-02 13:00:00+03:00  0.0210
2018-01-02 14:00:00+03:00  0.0353
...                           ...
2023-09-22 14:00:00+03:00 -0.3600
2023-09-22 15:00:00+03:00 -0.1800
2023-09-22 16:00:00+03:00  0.2400
2023-09-22 17:00:00+03:00 -0.3000
2023-09-22 18:00:00+03:00 -0.0400

[14226 rows x 1 columns]
ADF Statistic: -18.260230314341218
p-value: 2.332033535288983e-30
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood               12257.874
Date:                Sun, 24 Dec 2023   AIC                         -24451.748
Time:                        18:25:47   BIC                         -24209.738
Sample:                             0   HQIC                        -24371.243
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.0046      0.290      0.016      0.987      -0.564       0.573
ar.L2          0.0226      0.005      4.747      0.000       0.013       0.032
ar.L3          0.0322      0.007      4.452      0.000       0.018       0.046
ar.L4          0.0038      0.010      0.373      0.709      -0.016       0.024
ar.L5          0.0309      0.004      7.737      0.000       0.023       0.039
ar.L6          0.0142      0.010      1.456      0.145      -0.005       0.033
ar.L7         -0.0021      0.005     -0.417      0.676      -0.012       0.008
ar.L8         -0.0079      0.005     -1.753      0.080      -0.017       0.001
ar.L9          0.0194      0.004      4.357      0.000       0.011       0.028
ar.L10        -0.0247      0.006     -3.930      0.000      -0.037      -0.012
ar.L11        -0.0094      0.008     -1.167      0.243      -0.025       0.006
ar.L12        -0.0144      0.005     -2.816      0.005      -0.024      -0.004
ar.L13         0.0020      0.006      0.339      0.734      -0.009       0.013
ar.L14        -0.0115      0.004     -3.037      0.002      -0.019      -0.004
ar.L15         0.0107      0.005      2.183      0.029       0.001       0.020
ar.L16        -0.0063      0.006     -1.126      0.260      -0.017       0.005
ar.L17         0.0333      0.005      7.401      0.000       0.024       0.042
ar.L18         0.0002      0.011      0.015      0.988      -0.021       0.021
ar.L19        -0.0043      0.004     -1.136      0.256      -0.012       0.003
ar.L20        -0.0042      0.004     -0.983      0.325      -0.013       0.004
ar.L21         0.0112      0.004      2.641      0.008       0.003       0.019
ar.L22         0.0141      0.005      2.586      0.010       0.003       0.025
ar.L23         0.0063      0.006      1.077      0.281      -0.005       0.018
ar.L24        -0.0071      0.004     -1.611      0.107      -0.016       0.002
ar.L25        -0.0298      0.004     -7.019      0.000      -0.038      -0.021
ar.L26         0.0101      0.010      1.068      0.286      -0.008       0.029
ar.L27         0.0172      0.005      3.194      0.001       0.007       0.028
ar.L28        -0.0074      0.007     -1.099      0.272      -0.021       0.006
ar.L29        -0.0020      0.004     -0.497      0.619      -0.010       0.006
ar.L30         0.0136      0.003      3.910      0.000       0.007       0.020
ma.L1          0.0036      0.290      0.012      0.990      -0.565       0.572
sigma2         0.0104   2.68e-05    389.963      0.000       0.010       0.011
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           2079922.52
Prob(Q):                              0.97   Prob(JB):                         0.00
Heteroskedasticity (H):              15.46   Skew:                            -0.44
Prob(H) (two-sided):                  0.00   Kurtosis:                        62.23
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    30.763473
14228    30.763770
14229    30.763265
14230    30.785909
14231    30.774089
14232    30.782824
14233    30.775300
14234    30.775289
14235    30.777172
14236    30.793909
Name: predicted_mean, dtype: float64
       lower price  upper price
14227    30.563124    30.963822
14228    30.479272    31.048268
14229    30.411714    31.114816
14230    30.374857    31.196961
14231    30.310688    31.237491
14232    30.269660    31.295989
14233    30.215530    31.335069
14234    30.172526    31.378052
14235    30.134648    31.419696
14236    30.112590    31.475227

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 1.0563051480457972
In [683]:
arima_forecast('ARCLK', 30, 1, 1)

                              price
timestamp                          
2023-09-20 09:00:00+03:00  154.5277
2023-09-20 10:00:00+03:00  155.6090
2023-09-20 11:00:00+03:00  154.9209
2023-09-20 12:00:00+03:00  154.4294
2023-09-20 13:00:00+03:00  153.9379
2023-09-20 14:00:00+03:00  153.5447
2023-09-20 15:00:00+03:00  152.3651
2023-09-20 16:00:00+03:00  152.0702
2023-09-20 17:00:00+03:00  151.4804
2023-09-20 18:00:00+03:00  151.4804
2023-09-21 09:00:00+03:00  151.4804
2023-09-21 10:00:00+03:00  149.0229
2023-09-21 11:00:00+03:00  149.4161
2023-09-21 12:00:00+03:00  148.9246
2023-09-21 13:00:00+03:00  150.0059
2023-09-21 14:00:00+03:00  153.9379
2023-09-21 15:00:00+03:00  153.0532
2023-09-21 16:00:00+03:00  153.7413
2023-09-21 17:00:00+03:00  155.5107
2023-09-21 18:00:00+03:00  155.6090
2023-09-22 09:00:00+03:00  155.7073
2023-09-22 10:00:00+03:00  154.8226
2023-09-22 11:00:00+03:00  155.3141
2023-09-22 12:00:00+03:00  155.3141
2023-09-22 13:00:00+03:00  155.9039
2023-09-22 14:00:00+03:00  155.2158
2023-09-22 15:00:00+03:00  155.0192
2023-09-22 16:00:00+03:00  155.5107
2023-09-22 17:00:00+03:00  153.3481
2023-09-22 18:00:00+03:00  154.0362
Mean of the first 10 values: price    156.59
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  155.6
2023-09-25 10:00:00+03:00  155.5
2023-09-25 11:00:00+03:00  156.3
2023-09-25 12:00:00+03:00  156.3
2023-09-25 13:00:00+03:00  157.6
2023-09-25 14:00:00+03:00  156.4
2023-09-25 15:00:00+03:00  156.5
2023-09-25 16:00:00+03:00  157.3
2023-09-25 17:00:00+03:00  157.2
2023-09-25 18:00:00+03:00  157.2
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.0853
2018-01-02 11:00:00+03:00 -0.1195
2018-01-02 12:00:00+03:00 -0.0171
2018-01-02 13:00:00+03:00  0.0000
2018-01-02 14:00:00+03:00  0.1025
...                           ...
2023-09-22 14:00:00+03:00 -0.6881
2023-09-22 15:00:00+03:00 -0.1966
2023-09-22 16:00:00+03:00  0.4915
2023-09-22 17:00:00+03:00 -2.1626
2023-09-22 18:00:00+03:00  0.6881

[14226 rows x 1 columns]
ADF Statistic: -17.973416452398276
p-value: 2.7906236931361954e-30
Critical Values: {'1%': -3.4308111170220643, '5%': -2.8617437929148606, '10%': -2.566878474199101}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood              -11274.773
Date:                Sun, 24 Dec 2023   AIC                          22613.545
Time:                        18:26:10   BIC                          22855.556
Sample:                             0   HQIC                         22694.050
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.0029      0.184      0.016      0.987      -0.357       0.363
ar.L2         -0.0109      0.004     -2.787      0.005      -0.019      -0.003
ar.L3          0.0202      0.004      5.354      0.000       0.013       0.028
ar.L4          0.0090      0.005      1.754      0.079      -0.001       0.019
ar.L5         -0.0254      0.004     -6.803      0.000      -0.033      -0.018
ar.L6         -0.0063      0.006     -1.069      0.285      -0.018       0.005
ar.L7          0.0386      0.004      9.719      0.000       0.031       0.046
ar.L8         -0.0011      0.008     -0.139      0.890      -0.017       0.015
ar.L9          0.0147      0.003      4.304      0.000       0.008       0.021
ar.L10         0.0192      0.004      4.767      0.000       0.011       0.027
ar.L11        -0.0081      0.005     -1.636      0.102      -0.018       0.002
ar.L12        -0.0262      0.004     -6.660      0.000      -0.034      -0.018
ar.L13        -0.0221      0.006     -3.631      0.000      -0.034      -0.010
ar.L14        -0.0221      0.006     -3.948      0.000      -0.033      -0.011
ar.L15         0.0004      0.005      0.067      0.947      -0.010       0.011
ar.L16         0.0208      0.004      5.731      0.000       0.014       0.028
ar.L17         0.0117      0.005      2.185      0.029       0.001       0.022
ar.L18         0.0047      0.004      1.073      0.283      -0.004       0.013
ar.L19         0.0238      0.004      6.302      0.000       0.016       0.031
ar.L20         0.0167      0.005      3.100      0.002       0.006       0.027
ar.L21        -0.0079      0.005     -1.535      0.125      -0.018       0.002
ar.L22         0.0086      0.004      2.085      0.037       0.001       0.017
ar.L23         0.0074      0.004      1.768      0.077      -0.001       0.016
ar.L24        -0.0053      0.004     -1.354      0.176      -0.013       0.002
ar.L25         0.0009      0.004      0.224      0.823      -0.007       0.009
ar.L26        -0.0186      0.004     -4.849      0.000      -0.026      -0.011
ar.L27        -0.0298      0.005     -5.752      0.000      -0.040      -0.020
ar.L28        -0.0068      0.007     -1.033      0.302      -0.020       0.006
ar.L29         0.0057      0.004      1.457      0.145      -0.002       0.013
ar.L30         0.0216      0.004      6.164      0.000       0.015       0.028
ma.L1          0.0031      0.184      0.017      0.987      -0.357       0.363
sigma2         0.2857      0.001    316.033      0.000       0.284       0.287
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):            528083.02
Prob(Q):                              0.97   Prob(JB):                         0.00
Heteroskedasticity (H):              81.27   Skew:                             1.08
Prob(H) (two-sided):                  0.00   Kurtosis:                        32.77
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    154.047863
14228    154.066918
14229    154.064732
14230    154.185188
14231    154.363477
14232    154.340060
14233    154.311602
14234    154.424872
14235    154.491393
14236    154.507060
Name: predicted_mean, dtype: float64
       lower price  upper price
14227   153.000245   155.095480
14228   152.580948   155.552887
14229   152.249559   155.879904
14230   152.081378   156.288997
14231   152.001712   156.725241
14232   151.756914   156.923205
14233   151.527064   157.096140
14234   151.437841   157.411904
14235   151.315326   157.667460
14236   151.148154   157.865965

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 2.3726371950192835
In [684]:
arima_forecast('ASELS', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  39.60
2023-09-20 10:00:00+03:00  39.84
2023-09-20 11:00:00+03:00  39.34
2023-09-20 12:00:00+03:00  39.44
2023-09-20 13:00:00+03:00  39.32
2023-09-20 14:00:00+03:00  39.16
2023-09-20 15:00:00+03:00  38.96
2023-09-20 16:00:00+03:00  38.54
2023-09-20 17:00:00+03:00  38.08
2023-09-20 18:00:00+03:00  38.00
2023-09-21 09:00:00+03:00  37.86
2023-09-21 10:00:00+03:00  37.64
2023-09-21 11:00:00+03:00  37.70
2023-09-21 12:00:00+03:00  37.90
2023-09-21 13:00:00+03:00  37.98
2023-09-21 14:00:00+03:00  39.02
2023-09-21 15:00:00+03:00  38.98
2023-09-21 16:00:00+03:00  39.24
2023-09-21 17:00:00+03:00  39.68
2023-09-21 18:00:00+03:00  39.86
2023-09-22 09:00:00+03:00  39.86
2023-09-22 10:00:00+03:00  40.94
2023-09-22 11:00:00+03:00  40.78
2023-09-22 12:00:00+03:00  40.66
2023-09-22 13:00:00+03:00  40.52
2023-09-22 14:00:00+03:00  40.30
2023-09-22 15:00:00+03:00  40.16
2023-09-22 16:00:00+03:00  40.24
2023-09-22 17:00:00+03:00  40.64
2023-09-22 18:00:00+03:00  40.52
Mean of the first 10 values: price    41.686
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  40.62
2023-09-25 10:00:00+03:00  41.54
2023-09-25 11:00:00+03:00  41.82
2023-09-25 12:00:00+03:00  41.54
2023-09-25 13:00:00+03:00  41.88
2023-09-25 14:00:00+03:00  41.62
2023-09-25 15:00:00+03:00  41.56
2023-09-25 16:00:00+03:00  41.66
2023-09-25 17:00:00+03:00  42.32
2023-09-25 18:00:00+03:00  42.30
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.0144
2018-01-02 11:00:00+03:00 -0.0144
2018-01-02 12:00:00+03:00  0.0289
2018-01-02 13:00:00+03:00  0.0048
2018-01-02 14:00:00+03:00 -0.0096
...                           ...
2023-09-22 14:00:00+03:00 -0.2200
2023-09-22 15:00:00+03:00 -0.1400
2023-09-22 16:00:00+03:00  0.0800
2023-09-22 17:00:00+03:00  0.4000
2023-09-22 18:00:00+03:00 -0.1200

[14116 rows x 1 columns]
ADF Statistic: -17.371049345876518
p-value: 5.1374824316080754e-30
Critical Values: {'1%': -3.4308147547204983, '5%': -2.8617454004875986, '10%': -2.566879329894271}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14117
Model:                ARIMA(30, 1, 1)   Log Likelihood                7903.400
Date:                Sun, 24 Dec 2023   AIC                         -15742.800
Time:                        18:26:40   BIC                         -15501.038
Sample:                             0   HQIC                        -15662.347
                              - 14117                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.0078      2.065      0.004      0.997      -4.040       4.055
ar.L2          0.0058      0.032      0.181      0.856      -0.057       0.068
ar.L3          0.0110      0.012      0.908      0.364      -0.013       0.035
ar.L4         -0.0402      0.023     -1.779      0.075      -0.084       0.004
ar.L5          0.0035      0.083      0.042      0.967      -0.159       0.166
ar.L6          0.0362      0.008      4.287      0.000       0.020       0.053
ar.L7          0.0135      0.075      0.180      0.857      -0.133       0.160
ar.L8         -0.0188      0.027     -0.696      0.486      -0.072       0.034
ar.L9          0.0367      0.039      0.934      0.350      -0.040       0.114
ar.L10        -0.0412      0.076     -0.540      0.589      -0.191       0.108
ar.L11        -0.0257      0.086     -0.300      0.764      -0.194       0.142
ar.L12        -0.0430      0.053     -0.815      0.415      -0.146       0.060
ar.L13        -0.0064      0.088     -0.073      0.942      -0.179       0.167
ar.L14        -0.0175      0.013     -1.304      0.192      -0.044       0.009
ar.L15         0.0224      0.037      0.614      0.539      -0.049       0.094
ar.L16         0.0158      0.047      0.338      0.736      -0.076       0.107
ar.L17         0.0127      0.033      0.385      0.700      -0.052       0.077
ar.L18         0.0052      0.026      0.199      0.842      -0.046       0.056
ar.L19         0.0272      0.011      2.468      0.014       0.006       0.049
ar.L20         0.0195      0.056      0.346      0.729      -0.091       0.130
ar.L21        -0.0019      0.040     -0.048      0.962      -0.081       0.077
ar.L22         0.0241      0.006      4.152      0.000       0.013       0.035
ar.L23        -0.0084      0.050     -0.166      0.868      -0.107       0.090
ar.L24        -0.0384      0.018     -2.124      0.034      -0.074      -0.003
ar.L25         0.0078      0.079      0.099      0.921      -0.148       0.164
ar.L26        -0.0028      0.017     -0.160      0.873      -0.036       0.031
ar.L27         0.0141      0.007      1.900      0.057      -0.000       0.029
ar.L28         0.0096      0.029      0.326      0.745      -0.048       0.067
ar.L29         0.0144      0.020      0.720      0.471      -0.025       0.054
ar.L30        -0.0016      0.030     -0.055      0.956      -0.060       0.057
ma.L1          0.0076      2.065      0.004      0.997      -4.040       4.055
sigma2         0.0191   5.54e-05    345.000      0.000       0.019       0.019
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           1001031.56
Prob(Q):                              0.98   Prob(JB):                         0.00
Heteroskedasticity (H):              32.31   Skew:                             1.59
Prob(H) (two-sided):                  0.00   Kurtosis:                        44.13
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14117    40.556194
14118    40.507075
14119    40.486833
14120    40.467543
14121    40.526681
14122    40.550885
14123    40.586415
14124    40.657192
14125    40.645904
14126    40.607886
Name: predicted_mean, dtype: float64
       lower price  upper price
14117    40.285276    40.827112
14118    40.120978    40.893171
14119    40.011830    40.961835
14120    39.916309    41.018777
14121    39.913363    41.139999
14122    39.880923    41.220847
14123    39.860543    41.312286
14124    39.878103    41.436282
14125    39.818510    41.473299
14126    39.731610    41.484162

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 1.2079867998018134
In [548]:
arima_forecast('BIMAS', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  276.2
2023-09-20 10:00:00+03:00  275.6
2023-09-20 11:00:00+03:00  274.5
2023-09-20 12:00:00+03:00  272.0
2023-09-20 13:00:00+03:00  272.8
2023-09-20 14:00:00+03:00  271.8
2023-09-20 15:00:00+03:00  270.7
2023-09-20 16:00:00+03:00  270.8
2023-09-20 17:00:00+03:00  270.4
2023-09-20 18:00:00+03:00  270.0
2023-09-21 09:00:00+03:00  270.0
2023-09-21 10:00:00+03:00  270.4
2023-09-21 11:00:00+03:00  270.3
2023-09-21 12:00:00+03:00  270.2
2023-09-21 13:00:00+03:00  270.6
2023-09-21 14:00:00+03:00  276.0
2023-09-21 15:00:00+03:00  273.8
2023-09-21 16:00:00+03:00  274.3
2023-09-21 17:00:00+03:00  276.0
2023-09-21 18:00:00+03:00  275.7
2023-09-22 09:00:00+03:00  278.0
2023-09-22 10:00:00+03:00  274.1
2023-09-22 11:00:00+03:00  274.3
2023-09-22 12:00:00+03:00  276.2
2023-09-22 13:00:00+03:00  275.4
2023-09-22 14:00:00+03:00  274.0
2023-09-22 15:00:00+03:00  273.0
2023-09-22 16:00:00+03:00  272.4
2023-09-22 17:00:00+03:00  273.4
2023-09-22 18:00:00+03:00  273.4
Mean of the first 10 values: price    274.68
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  275.0
2023-09-25 10:00:00+03:00  272.1
2023-09-25 11:00:00+03:00  272.9
2023-09-25 12:00:00+03:00  273.9
2023-09-25 13:00:00+03:00  274.2
2023-09-25 14:00:00+03:00  273.9
2023-09-25 15:00:00+03:00  273.7
2023-09-25 16:00:00+03:00  274.8
2023-09-25 17:00:00+03:00  278.6
2023-09-25 18:00:00+03:00  277.7
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.3467
2018-01-02 11:00:00+03:00 -0.1226
2018-01-02 12:00:00+03:00 -0.1833
2018-01-02 13:00:00+03:00  0.1426
2018-01-02 14:00:00+03:00  0.1428
...                           ...
2023-09-22 14:00:00+03:00 -1.4000
2023-09-22 15:00:00+03:00 -1.0000
2023-09-22 16:00:00+03:00 -0.6000
2023-09-22 17:00:00+03:00  1.0000
2023-09-22 18:00:00+03:00  0.0000

[14226 rows x 1 columns]
ADF Statistic: -17.75951732138609
p-value: 3.3431484388348195e-30
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood              -15556.978
Date:                Sun, 24 Dec 2023   AIC                          31177.957
Time:                        01:21:55   BIC                          31419.967
Sample:                             0   HQIC                         31258.461
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.3678      0.258      1.425      0.154      -0.138       0.874
ar.L2          0.0170      0.014      1.256      0.209      -0.010       0.044
ar.L3          0.0003      0.004      0.083      0.934      -0.008       0.009
ar.L4          0.0167      0.004      4.369      0.000       0.009       0.024
ar.L5          0.0206      0.006      3.501      0.000       0.009       0.032
ar.L6          0.0375      0.008      4.661      0.000       0.022       0.053
ar.L7          0.0032      0.013      0.245      0.807      -0.023       0.029
ar.L8          0.0006      0.008      0.081      0.935      -0.015       0.016
ar.L9          0.0067      0.005      1.345      0.179      -0.003       0.016
ar.L10         0.0309      0.004      7.622      0.000       0.023       0.039
ar.L11        -0.0500      0.010     -5.173      0.000      -0.069      -0.031
ar.L12        -0.0401      0.010     -3.945      0.000      -0.060      -0.020
ar.L13         0.0171      0.015      1.160      0.246      -0.012       0.046
ar.L14        -0.0263      0.005     -5.547      0.000      -0.036      -0.017
ar.L15        -0.0132      0.008     -1.573      0.116      -0.030       0.003
ar.L16        -0.0098      0.008     -1.255      0.210      -0.025       0.005
ar.L17         0.0273      0.006      4.273      0.000       0.015       0.040
ar.L18         0.0052      0.007      0.787      0.431      -0.008       0.018
ar.L19        -0.0002      0.006     -0.038      0.970      -0.011       0.011
ar.L20         0.0026      0.004      0.662      0.508      -0.005       0.010
ar.L21         0.0213      0.005      4.533      0.000       0.012       0.031
ar.L22        -0.0032      0.007     -0.457      0.647      -0.017       0.011
ar.L23        -0.0035      0.005     -0.752      0.452      -0.013       0.006
ar.L24         0.0258      0.004      6.663      0.000       0.018       0.033
ar.L25        -0.0404      0.007     -5.587      0.000      -0.055      -0.026
ar.L26         0.0162      0.008      1.944      0.052      -0.000       0.033
ar.L27         0.0077      0.004      2.065      0.039       0.000       0.015
ar.L28        -0.0033      0.005     -0.638      0.524      -0.014       0.007
ar.L29         0.0028      0.005      0.596      0.551      -0.006       0.012
ar.L30        -0.0134      0.003     -4.084      0.000      -0.020      -0.007
ma.L1         -0.4191      0.258     -1.624      0.104      -0.925       0.087
sigma2         0.5217      0.001    386.592      0.000       0.519       0.524
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           3173787.80
Prob(Q):                              0.95   Prob(JB):                         0.00
Heteroskedasticity (H):              35.34   Skew:                            -0.27
Prob(H) (two-sided):                  0.00   Kurtosis:                        76.17
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    273.183997
14228    272.854999
14229    272.903290
14230    273.131643
14231    272.970326
14232    272.983452
14233    273.199602
14234    273.340249
14235    273.398163
14236    273.562832
Name: predicted_mean, dtype: float64
       lower price  upper price
14227   271.768378   274.599616
14228   270.903623   274.806375
14229   270.535842   275.270739
14230   270.411862   275.851423
14231   269.928718   276.011935
14232   269.635947   276.330957
14233   269.546235   276.852970
14234   269.394821   277.285678
14235   269.176682   277.619644
14236   269.077166   278.048499

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 2.3101290285547753
In [553]:
arima_forecast('DOHOL', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  13.19
2023-09-20 10:00:00+03:00  13.15
2023-09-20 11:00:00+03:00  13.04
2023-09-20 12:00:00+03:00  13.00
2023-09-20 13:00:00+03:00  12.94
2023-09-20 14:00:00+03:00  13.02
2023-09-20 15:00:00+03:00  13.00
2023-09-20 16:00:00+03:00  12.92
2023-09-20 17:00:00+03:00  12.87
2023-09-20 18:00:00+03:00  12.86
2023-09-21 09:00:00+03:00  12.84
2023-09-21 10:00:00+03:00  12.86
2023-09-21 11:00:00+03:00  12.94
2023-09-21 12:00:00+03:00  12.89
2023-09-21 13:00:00+03:00  12.87
2023-09-21 14:00:00+03:00  13.03
2023-09-21 15:00:00+03:00  13.00
2023-09-21 16:00:00+03:00  13.09
2023-09-21 17:00:00+03:00  13.28
2023-09-21 18:00:00+03:00  13.28
2023-09-22 09:00:00+03:00  13.28
2023-09-22 10:00:00+03:00  13.25
2023-09-22 11:00:00+03:00  13.32
2023-09-22 12:00:00+03:00  13.33
2023-09-22 13:00:00+03:00  13.32
2023-09-22 14:00:00+03:00  13.05
2023-09-22 15:00:00+03:00  13.06
2023-09-22 16:00:00+03:00  13.07
2023-09-22 17:00:00+03:00  13.05
2023-09-22 18:00:00+03:00  13.06
Mean of the first 10 values: price    13.307
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  13.14
2023-09-25 10:00:00+03:00  13.20
2023-09-25 11:00:00+03:00  13.19
2023-09-25 12:00:00+03:00  13.29
2023-09-25 13:00:00+03:00  13.32
2023-09-25 14:00:00+03:00  13.27
2023-09-25 15:00:00+03:00  13.31
2023-09-25 16:00:00+03:00  13.36
2023-09-25 17:00:00+03:00  13.50
2023-09-25 18:00:00+03:00  13.49
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.0082
2018-01-02 11:00:00+03:00  0.0000
2018-01-02 12:00:00+03:00  0.0000
2018-01-02 13:00:00+03:00  0.0079
2018-01-02 14:00:00+03:00 -0.0079
...                           ...
2023-09-22 14:00:00+03:00 -0.2700
2023-09-22 15:00:00+03:00  0.0100
2023-09-22 16:00:00+03:00  0.0100
2023-09-22 17:00:00+03:00 -0.0200
2023-09-22 18:00:00+03:00  0.0100

[14226 rows x 1 columns]
ADF Statistic: -18.874081160906577
p-value: 0.0
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood               23923.644
Date:                Sun, 24 Dec 2023   AIC                         -47783.288
Time:                        01:27:34   BIC                         -47541.278
Sample:                             0   HQIC                        -47702.784
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.0088      0.550      0.016      0.987      -1.068       1.086
ar.L2          0.0261      0.010      2.685      0.007       0.007       0.045
ar.L3          0.0329      0.014      2.294      0.022       0.005       0.061
ar.L4          0.0106      0.019      0.567      0.571      -0.026       0.047
ar.L5         -0.0046      0.007     -0.701      0.483      -0.018       0.008
ar.L6          0.0234      0.004      5.506      0.000       0.015       0.032
ar.L7         -0.0176      0.013     -1.310      0.190      -0.044       0.009
ar.L8         -0.0027      0.010     -0.265      0.791      -0.023       0.017
ar.L9          0.0064      0.004      1.717      0.086      -0.001       0.014
ar.L10        -0.0485      0.005    -10.542      0.000      -0.058      -0.040
ar.L11        -0.0365      0.027     -1.360      0.174      -0.089       0.016
ar.L12        -0.0179      0.020     -0.881      0.378      -0.058       0.022
ar.L13         0.0249      0.010      2.427      0.015       0.005       0.045
ar.L14        -0.0054      0.014     -0.388      0.698      -0.033       0.022
ar.L15        -0.0314      0.005     -6.842      0.000      -0.040      -0.022
ar.L16         0.0160      0.018      0.900      0.368      -0.019       0.051
ar.L17        -0.0071      0.009     -0.761      0.447      -0.026       0.011
ar.L18        -0.0009      0.006     -0.154      0.877      -0.012       0.010
ar.L19        -0.0102      0.004     -2.656      0.008      -0.018      -0.003
ar.L20         0.0302      0.007      4.290      0.000       0.016       0.044
ar.L21        -0.0391      0.017     -2.324      0.020      -0.072      -0.006
ar.L22         0.0061      0.022      0.274      0.784      -0.037       0.049
ar.L23         0.0258      0.005      4.828      0.000       0.015       0.036
ar.L24        -0.0031      0.014     -0.217      0.828      -0.031       0.025
ar.L25         0.0205      0.004      4.794      0.000       0.012       0.029
ar.L26        -0.0177      0.012     -1.501      0.133      -0.041       0.005
ar.L27         0.0452      0.010      4.389      0.000       0.025       0.065
ar.L28        -0.0216      0.025     -0.859      0.390      -0.071       0.028
ar.L29         0.0154      0.013      1.219      0.223      -0.009       0.040
ar.L30         0.0066      0.009      0.756      0.450      -0.011       0.024
ma.L1          0.0075      0.549      0.014      0.989      -1.069       1.084
sigma2         0.0020   6.64e-06    305.113      0.000       0.002       0.002
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):            625255.01
Prob(Q):                              0.97   Prob(JB):                         0.00
Heteroskedasticity (H):              58.18   Skew:                             0.29
Prob(H) (two-sided):                  0.00   Kurtosis:                        35.47
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    13.055134
14228    13.052259
14229    13.053699
14230    13.039222
14231    13.036180
14232    13.057271
14233    13.057245
14234    13.060252
14235    13.060586
14236    13.057421
Name: predicted_mean, dtype: float64
       lower price  upper price
14227    12.966898    13.143370
14228    12.926453    13.178064
14229    12.897849    13.209549
14230    12.856727    13.221717
14231    12.829966    13.242395
14232    12.829894    13.284648
14233    12.809650    13.304840
14234    12.794500    13.326003
14235    12.777902    13.343270
14236    12.758562    13.356279

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.2775334440525403
In [554]:
arima_forecast('EKGYO', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00   7.92
2023-09-20 10:00:00+03:00   8.00
2023-09-20 11:00:00+03:00   7.97
2023-09-20 12:00:00+03:00   7.95
2023-09-20 13:00:00+03:00   7.97
2023-09-20 14:00:00+03:00   8.01
2023-09-20 15:00:00+03:00   7.99
2023-09-20 16:00:00+03:00   7.94
2023-09-20 17:00:00+03:00   7.85
2023-09-20 18:00:00+03:00   7.80
2023-09-21 09:00:00+03:00   7.75
2023-09-21 10:00:00+03:00   7.82
2023-09-21 11:00:00+03:00   7.83
2023-09-21 12:00:00+03:00   7.78
2023-09-21 13:00:00+03:00   7.80
2023-09-21 14:00:00+03:00   7.92
2023-09-21 15:00:00+03:00   7.88
2023-09-21 16:00:00+03:00   7.91
2023-09-21 17:00:00+03:00   8.01
2023-09-21 18:00:00+03:00   8.03
2023-09-22 09:00:00+03:00   8.03
2023-09-22 10:00:00+03:00   8.02
2023-09-22 11:00:00+03:00   8.05
2023-09-22 12:00:00+03:00   8.03
2023-09-22 13:00:00+03:00   7.98
2023-09-22 14:00:00+03:00   7.91
2023-09-22 15:00:00+03:00   7.89
2023-09-22 16:00:00+03:00   7.89
2023-09-22 17:00:00+03:00   7.90
2023-09-22 18:00:00+03:00   7.91
Mean of the first 10 values: price    8.061
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00   7.94
2023-09-25 10:00:00+03:00   8.08
2023-09-25 11:00:00+03:00   8.08
2023-09-25 12:00:00+03:00   8.06
2023-09-25 13:00:00+03:00   8.05
2023-09-25 14:00:00+03:00   8.00
2023-09-25 15:00:00+03:00   7.99
2023-09-25 16:00:00+03:00   8.05
2023-09-25 17:00:00+03:00   8.18
2023-09-25 18:00:00+03:00   8.18
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.0246
2018-01-02 11:00:00+03:00 -0.0081
2018-01-02 12:00:00+03:00  0.0081
2018-01-02 13:00:00+03:00  0.0000
2018-01-02 14:00:00+03:00  0.0081
...                           ...
2023-09-22 14:00:00+03:00 -0.0700
2023-09-22 15:00:00+03:00 -0.0200
2023-09-22 16:00:00+03:00  0.0000
2023-09-22 17:00:00+03:00  0.0100
2023-09-22 18:00:00+03:00  0.0100

[14226 rows x 1 columns]
ADF Statistic: -16.268374896071755
p-value: 3.5083175413537314e-29
Critical Values: {'1%': -3.430811052000141, '5%': -2.8617437641803356, '10%': -2.566878458903999}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood               27355.560
Date:                Sun, 24 Dec 2023   AIC                         -54647.121
Time:                        01:29:04   BIC                         -54405.110
Sample:                             0   HQIC                        -54566.616
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.0188      0.559      0.034      0.973      -1.076       1.114
ar.L2          0.0107      0.021      0.505      0.614      -0.031       0.052
ar.L3         -0.0026      0.007     -0.364      0.716      -0.016       0.011
ar.L4          0.0130      0.004      3.331      0.001       0.005       0.021
ar.L5         -0.0052      0.008     -0.665      0.506      -0.021       0.010
ar.L6          0.0249      0.005      5.099      0.000       0.015       0.034
ar.L7         -0.0128      0.015     -0.860      0.390      -0.042       0.016
ar.L8          0.0152      0.009      1.691      0.091      -0.002       0.033
ar.L9          0.0466      0.009      5.033      0.000       0.028       0.065
ar.L10        -0.0067      0.025     -0.264      0.792      -0.057       0.043
ar.L11        -0.0423      0.006     -7.677      0.000      -0.053      -0.032
ar.L12        -0.0221      0.024     -0.920      0.357      -0.069       0.025
ar.L13        -0.0182      0.012     -1.517      0.129      -0.042       0.005
ar.L14        -0.0027      0.011     -0.253      0.800      -0.024       0.018
ar.L15         0.0079      0.005      1.722      0.085      -0.001       0.017
ar.L16        -0.0004      0.006     -0.068      0.945      -0.013       0.012
ar.L17        -0.0132      0.004     -3.242      0.001      -0.021      -0.005
ar.L18        -0.0174      0.009     -2.035      0.042      -0.034      -0.001
ar.L19         0.0515      0.010      5.097      0.000       0.032       0.071
ar.L20         0.0106      0.030      0.358      0.720      -0.048       0.069
ar.L21         0.0189      0.006      3.042      0.002       0.007       0.031
ar.L22         0.0053      0.011      0.484      0.628      -0.016       0.027
ar.L23        -0.0054      0.005     -1.039      0.299      -0.016       0.005
ar.L24         0.0029      0.005      0.558      0.577      -0.007       0.013
ar.L25        -0.0137      0.004     -3.261      0.001      -0.022      -0.005
ar.L26        -0.0077      0.008     -0.909      0.364      -0.024       0.009
ar.L27         0.0236      0.006      3.695      0.000       0.011       0.036
ar.L28        -0.0186      0.015     -1.274      0.203      -0.047       0.010
ar.L29         0.0129      0.011      1.151      0.250      -0.009       0.035
ar.L30        -0.0061      0.008     -0.757      0.449      -0.022       0.010
ma.L1          0.0184      0.558      0.033      0.974      -1.076       1.113
sigma2         0.0013   3.69e-06    338.753      0.000       0.001       0.001
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           1254139.08
Prob(Q):                              0.99   Prob(JB):                         0.00
Heteroskedasticity (H):              25.79   Skew:                             0.27
Prob(H) (two-sided):                  0.00   Kurtosis:                        48.99
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    7.909854
14228    7.908316
14229    7.904935
14230    7.902015
14231    7.905615
14232    7.904336
14233    7.909942
14234    7.917166
14235    7.924528
14236    7.925618
Name: predicted_mean, dtype: float64
       lower price  upper price
14227     7.840532     7.979175
14228     7.808444     8.008189
14229     7.781411     8.028460
14230     7.758759     8.045271
14231     7.744626     8.066603
14232     7.727513     8.081159
14233     7.717924     8.101960
14234     7.711361     8.122971
14235     7.705444     8.143612
14236     7.692939     8.158298

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.16432282382328908
In [555]:
arima_forecast('EREGL', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  42.34
2023-09-20 10:00:00+03:00  42.32
2023-09-20 11:00:00+03:00  42.00
2023-09-20 12:00:00+03:00  41.98
2023-09-20 13:00:00+03:00  41.94
2023-09-20 14:00:00+03:00  41.90
2023-09-20 15:00:00+03:00  41.80
2023-09-20 16:00:00+03:00  41.44
2023-09-20 17:00:00+03:00  41.08
2023-09-20 18:00:00+03:00  41.00
2023-09-21 09:00:00+03:00  40.78
2023-09-21 10:00:00+03:00  40.48
2023-09-21 11:00:00+03:00  40.48
2023-09-21 12:00:00+03:00  40.38
2023-09-21 13:00:00+03:00  40.42
2023-09-21 14:00:00+03:00  41.26
2023-09-21 15:00:00+03:00  41.10
2023-09-21 16:00:00+03:00  41.28
2023-09-21 17:00:00+03:00  41.72
2023-09-21 18:00:00+03:00  41.74
2023-09-22 09:00:00+03:00  41.84
2023-09-22 10:00:00+03:00  42.08
2023-09-22 11:00:00+03:00  42.18
2023-09-22 12:00:00+03:00  42.32
2023-09-22 13:00:00+03:00  42.14
2023-09-22 14:00:00+03:00  42.84
2023-09-22 15:00:00+03:00  43.06
2023-09-22 16:00:00+03:00  43.22
2023-09-22 17:00:00+03:00  43.18
2023-09-22 18:00:00+03:00  43.28
Mean of the first 10 values: price    45.784
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  43.44
2023-09-25 10:00:00+03:00  43.98
2023-09-25 11:00:00+03:00  45.16
2023-09-25 12:00:00+03:00  45.74
2023-09-25 13:00:00+03:00  46.80
2023-09-25 14:00:00+03:00  46.68
2023-09-25 15:00:00+03:00  46.60
2023-09-25 16:00:00+03:00  46.74
2023-09-25 17:00:00+03:00  46.38
2023-09-25 18:00:00+03:00  46.32
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.0178
2018-01-02 11:00:00+03:00 -0.0237
2018-01-02 12:00:00+03:00  0.0000
2018-01-02 13:00:00+03:00  0.0118
2018-01-02 14:00:00+03:00  0.0237
...                           ...
2023-09-22 14:00:00+03:00  0.7000
2023-09-22 15:00:00+03:00  0.2200
2023-09-22 16:00:00+03:00  0.1600
2023-09-22 17:00:00+03:00 -0.0400
2023-09-22 18:00:00+03:00  0.1000

[14226 rows x 1 columns]
ADF Statistic: -18.459300597620004
p-value: 2.147942075114296e-30
Critical Values: {'1%': -3.430811052000141, '5%': -2.8617437641803356, '10%': -2.566878458903999}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood                4151.688
Date:                Sun, 24 Dec 2023   AIC                          -8239.376
Time:                        01:31:40   BIC                          -7997.365
Sample:                             0   HQIC                         -8158.871
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.5443      0.174      3.125      0.002       0.203       0.886
ar.L2         -0.0439      0.008     -5.169      0.000      -0.061      -0.027
ar.L3          0.0733      0.005     13.326      0.000       0.062       0.084
ar.L4         -0.0339      0.011     -2.959      0.003      -0.056      -0.011
ar.L5         -0.0156      0.004     -3.737      0.000      -0.024      -0.007
ar.L6          0.0134      0.006      2.192      0.028       0.001       0.025
ar.L7         -0.0066      0.005     -1.317      0.188      -0.017       0.003
ar.L8          0.0188      0.005      3.921      0.000       0.009       0.028
ar.L9          0.0239      0.005      4.460      0.000       0.013       0.034
ar.L10        -0.0282      0.007     -3.840      0.000      -0.043      -0.014
ar.L11        -0.0537      0.005    -11.201      0.000      -0.063      -0.044
ar.L12        -0.0052      0.011     -0.461      0.645      -0.027       0.017
ar.L13         0.0697      0.008      8.887      0.000       0.054       0.085
ar.L14        -0.0152      0.010     -1.473      0.141      -0.035       0.005
ar.L15         0.0032      0.005      0.654      0.513      -0.006       0.013
ar.L16         0.0029      0.006      0.517      0.605      -0.008       0.014
ar.L17        -0.0222      0.005     -4.349      0.000      -0.032      -0.012
ar.L18         0.0071      0.006      1.242      0.214      -0.004       0.018
ar.L19        -0.0009      0.006     -0.157      0.875      -0.012       0.010
ar.L20         0.0098      0.005      2.093      0.036       0.001       0.019
ar.L21         0.0031      0.004      0.769      0.442      -0.005       0.011
ar.L22        -0.0325      0.005     -5.951      0.000      -0.043      -0.022
ar.L23         0.0105      0.007      1.586      0.113      -0.002       0.023
ar.L24        -0.0036      0.005     -0.716      0.474      -0.014       0.006
ar.L25        -0.0086      0.005     -1.699      0.089      -0.019       0.001
ar.L26        -0.0086      0.006     -1.482      0.138      -0.020       0.003
ar.L27         0.0298      0.005      5.467      0.000       0.019       0.041
ar.L28        -0.0086      0.007     -1.293      0.196      -0.022       0.004
ar.L29         0.0149      0.005      2.834      0.005       0.005       0.025
ar.L30         0.0089      0.007      1.310      0.190      -0.004       0.022
ma.L1         -0.5014      0.174     -2.877      0.004      -0.843      -0.160
sigma2         0.0327    8.9e-05    367.097      0.000       0.032       0.033
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           2857002.15
Prob(Q):                              0.97   Prob(JB):                         0.00
Heteroskedasticity (H):              44.64   Skew:                             0.68
Prob(H) (two-sided):                  0.00   Kurtosis:                        72.41
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    43.300891
14228    43.319386
14229    43.299896
14230    43.313855
14231    43.339047
14232    43.349971
14233    43.325397
14234    43.241001
14235    43.245443
14236    43.242639
Name: predicted_mean, dtype: float64
       lower price  upper price
14227    42.946688    43.655094
14228    42.807600    43.831173
14229    42.672945    43.926848
14230    42.578938    44.048773
14231    42.509639    44.168454
14232    42.438698    44.261244
14233    42.338083    44.312711
14234    42.183471    44.298531
14235    42.120407    44.370478
14236    42.049925    44.435352

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 2.7410005144731233
In [556]:
arima_forecast('FROTO', 30, 1, 1)

                              price
timestamp                          
2023-09-20 09:00:00+03:00  814.0292
2023-09-20 10:00:00+03:00  824.0647
2023-09-20 11:00:00+03:00  811.2309
2023-09-20 12:00:00+03:00  806.5992
2023-09-20 13:00:00+03:00  806.9851
2023-09-20 14:00:00+03:00  805.2482
2023-09-20 15:00:00+03:00  800.3270
2023-09-20 16:00:00+03:00  796.2742
2023-09-20 17:00:00+03:00  796.8532
2023-09-20 18:00:00+03:00  797.2392
2023-09-21 09:00:00+03:00  797.1427
2023-09-21 10:00:00+03:00  789.1336
2023-09-21 11:00:00+03:00  790.9670
2023-09-21 12:00:00+03:00  790.9670
2023-09-21 13:00:00+03:00  795.5988
2023-09-21 14:00:00+03:00  807.5641
2023-09-21 15:00:00+03:00  803.8973
2023-09-21 16:00:00+03:00  806.9851
2023-09-21 17:00:00+03:00  819.5294
2023-09-21 18:00:00+03:00  819.7224
2023-09-22 09:00:00+03:00  819.7224
2023-09-22 10:00:00+03:00  815.4767
2023-09-22 11:00:00+03:00  816.7311
2023-09-22 12:00:00+03:00  818.6610
2023-09-22 13:00:00+03:00  815.0907
2023-09-22 14:00:00+03:00  810.1695
2023-09-22 15:00:00+03:00  808.0466
2023-09-22 16:00:00+03:00  809.2045
2023-09-22 17:00:00+03:00  812.4853
2023-09-22 18:00:00+03:00  809.9765
Mean of the first 10 values: price    812.85202
dtype: float64
                              price
timestamp                          
2023-09-25 09:00:00+03:00  813.8363
2023-09-25 10:00:00+03:00  804.9587
2023-09-25 11:00:00+03:00  806.6957
2023-09-25 12:00:00+03:00  812.0994
2023-09-25 13:00:00+03:00  811.1344
2023-09-25 14:00:00+03:00  805.7307
2023-09-25 15:00:00+03:00  809.3010
2023-09-25 16:00:00+03:00  823.9682
2023-09-25 17:00:00+03:00  820.3979
2023-09-25 18:00:00+03:00  820.3979
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.2094
2018-01-02 11:00:00+03:00  0.3142
2018-01-02 12:00:00+03:00  0.1744
2018-01-02 13:00:00+03:00 -0.1395
2018-01-02 14:00:00+03:00  0.2791
...                           ...
2023-09-22 14:00:00+03:00 -4.9212
2023-09-22 15:00:00+03:00 -2.1229
2023-09-22 16:00:00+03:00  1.1579
2023-09-22 17:00:00+03:00  3.2808
2023-09-22 18:00:00+03:00 -2.5088

[14225 rows x 1 columns]
ADF Statistic: -16.81526382151908
p-value: 1.1901683567553043e-29
Critical Values: {'1%': -3.43081118206233, '5%': -2.8617438216574906, '10%': -2.5668784894985173}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14226
Model:                ARIMA(30, 1, 1)   Log Likelihood              -32308.628
Date:                Sun, 24 Dec 2023   AIC                          64681.257
Time:                        01:33:31   BIC                          64923.265
Sample:                             0   HQIC                         64761.761
                              - 14226                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.4709      0.156      3.027      0.002       0.166       0.776
ar.L2         -0.0015      0.006     -0.257      0.797      -0.013       0.010
ar.L3          0.0100      0.005      2.074      0.038       0.001       0.019
ar.L4          0.0010      0.004      0.245      0.806      -0.007       0.009
ar.L5          0.0201      0.004      5.077      0.000       0.012       0.028
ar.L6          0.0397      0.006      6.616      0.000       0.028       0.051
ar.L7         -0.0262      0.009     -2.841      0.005      -0.044      -0.008
ar.L8          0.0032      0.005      0.691      0.490      -0.006       0.012
ar.L9          0.0407      0.004      9.344      0.000       0.032       0.049
ar.L10        -0.0092      0.008     -1.223      0.221      -0.024       0.006
ar.L11        -0.0509      0.004    -12.793      0.000      -0.059      -0.043
ar.L12         0.0006      0.008      0.067      0.946      -0.016       0.017
ar.L13        -0.0179      0.006     -3.205      0.001      -0.029      -0.007
ar.L14         0.0280      0.006      4.584      0.000       0.016       0.040
ar.L15        -0.0064      0.005     -1.341      0.180      -0.016       0.003
ar.L16        -0.0065      0.004     -1.530      0.126      -0.015       0.002
ar.L17         0.0294      0.004      6.794      0.000       0.021       0.038
ar.L18        -0.0282      0.006     -4.633      0.000      -0.040      -0.016
ar.L19         0.0002      0.005      0.034      0.973      -0.010       0.011
ar.L20         0.0066      0.004      1.499      0.134      -0.002       0.015
ar.L21        -0.0094      0.005     -2.059      0.040      -0.018      -0.000
ar.L22         0.0220      0.005      4.470      0.000       0.012       0.032
ar.L23        -0.0498      0.005     -9.267      0.000      -0.060      -0.039
ar.L24         0.0120      0.007      1.705      0.088      -0.002       0.026
ar.L25        -0.0025      0.005     -0.494      0.621      -0.013       0.007
ar.L26        -0.0040      0.005     -0.808      0.419      -0.014       0.006
ar.L27         0.0216      0.005      4.307      0.000       0.012       0.031
ar.L28        -0.0190      0.006     -3.208      0.001      -0.031      -0.007
ar.L29         0.0310      0.004      6.948      0.000       0.022       0.040
ar.L30         0.0055      0.006      0.918      0.359      -0.006       0.017
ma.L1         -0.4964      0.156     -3.190      0.001      -0.801      -0.191
sigma2         5.4995      0.016    337.019      0.000       5.468       5.532
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           1366530.73
Prob(Q):                              0.95   Prob(JB):                         0.00
Heteroskedasticity (H):             132.03   Skew:                             1.99
Prob(H) (two-sided):                  0.00   Kurtosis:                        50.85
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14226    809.659274
14227    809.096599
14228    809.567163
14229    808.910408
14230    808.981010
14231    808.885828
14232    808.734535
14233    809.114563
14234    808.231907
14235    808.142939
Name: predicted_mean, dtype: float64
       lower price  upper price
14226   805.062947   814.255602
14227   802.678787   815.514410
14228   801.776319   817.358007
14229   799.946368   817.874447
14230   798.975470   818.986550
14231   797.897657   819.874000
14232   796.756576   820.712494
14233   796.230032   821.999095
14234   794.498116   821.965698
14235   793.544460   822.741419

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 7.6597877426069605
In [685]:
arima_forecast('GUBRF', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  342.5
2023-09-20 10:00:00+03:00  342.9
2023-09-20 11:00:00+03:00  340.3
2023-09-20 12:00:00+03:00  339.5
2023-09-20 13:00:00+03:00  337.7
2023-09-20 14:00:00+03:00  337.5
2023-09-20 15:00:00+03:00  333.9
2023-09-20 16:00:00+03:00  329.5
2023-09-20 17:00:00+03:00  330.0
2023-09-20 18:00:00+03:00  328.5
2023-09-21 09:00:00+03:00  328.0
2023-09-21 10:00:00+03:00  328.6
2023-09-21 11:00:00+03:00  331.6
2023-09-21 12:00:00+03:00  329.6
2023-09-21 13:00:00+03:00  328.8
2023-09-21 14:00:00+03:00  332.8
2023-09-21 15:00:00+03:00  335.4
2023-09-21 16:00:00+03:00  337.7
2023-09-21 17:00:00+03:00  340.6
2023-09-21 18:00:00+03:00  340.5
2023-09-22 09:00:00+03:00  341.5
2023-09-22 10:00:00+03:00  339.6
2023-09-22 11:00:00+03:00  340.6
2023-09-22 12:00:00+03:00  338.7
2023-09-22 13:00:00+03:00  339.7
2023-09-22 14:00:00+03:00  337.2
2023-09-22 15:00:00+03:00  337.9
2023-09-22 16:00:00+03:00  337.4
2023-09-22 17:00:00+03:00  339.7
2023-09-22 18:00:00+03:00  339.5
Mean of the first 10 values: price    344.27
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  342.0
2023-09-25 10:00:00+03:00  342.7
2023-09-25 11:00:00+03:00  342.5
2023-09-25 12:00:00+03:00  342.4
2023-09-25 13:00:00+03:00  342.3
2023-09-25 14:00:00+03:00  340.7
2023-09-25 15:00:00+03:00  345.7
2023-09-25 16:00:00+03:00  347.9
2023-09-25 17:00:00+03:00  348.0
2023-09-25 18:00:00+03:00  348.5
                           price
timestamp                       
2018-01-02 10:00:00+03:00  -0.01
2018-01-02 11:00:00+03:00   0.03
2018-01-02 12:00:00+03:00   0.03
2018-01-02 13:00:00+03:00   0.01
2018-01-02 14:00:00+03:00   0.03
...                          ...
2023-09-22 14:00:00+03:00  -2.50
2023-09-22 15:00:00+03:00   0.70
2023-09-22 16:00:00+03:00  -0.50
2023-09-22 17:00:00+03:00   2.30
2023-09-22 18:00:00+03:00  -0.20

[14217 rows x 1 columns]
ADF Statistic: -14.876109874306591
p-value: 1.6356253220281786e-27
Critical Values: {'1%': -3.4308114098478204, '5%': -2.8617439223205756, '10%': -2.566878543080479}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14218
Model:                ARIMA(30, 1, 1)   Log Likelihood              -23328.547
Date:                Sun, 24 Dec 2023   AIC                          46721.095
Time:                        18:27:26   BIC                          46963.085
Sample:                             0   HQIC                         46801.595
                              - 14218                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.1915      0.087     -2.201      0.028      -0.362      -0.021
ar.L2         -0.0269      0.004     -6.076      0.000      -0.036      -0.018
ar.L3          0.0123      0.005      2.585      0.010       0.003       0.022
ar.L4         -0.0017      0.004     -0.421      0.674      -0.010       0.006
ar.L5          0.0113      0.004      3.194      0.001       0.004       0.018
ar.L6          0.0250      0.003      7.369      0.000       0.018       0.032
ar.L7          0.0248      0.005      5.411      0.000       0.016       0.034
ar.L8          0.0008      0.004      0.187      0.851      -0.008       0.009
ar.L9         -0.0118      0.004     -3.180      0.001      -0.019      -0.005
ar.L10         0.0591      0.003     20.924      0.000       0.054       0.065
ar.L11         0.0124      0.007      1.817      0.069      -0.001       0.026
ar.L12         0.0137      0.004      3.508      0.000       0.006       0.021
ar.L13         0.0380      0.003     12.046      0.000       0.032       0.044
ar.L14         0.0238      0.005      5.214      0.000       0.015       0.033
ar.L15         0.0257      0.003      7.461      0.000       0.019       0.032
ar.L16        -0.0064      0.004     -1.536      0.125      -0.014       0.002
ar.L17         0.0125      0.004      3.324      0.001       0.005       0.020
ar.L18         0.0125      0.004      3.138      0.002       0.005       0.020
ar.L19         0.0207      0.004      5.344      0.000       0.013       0.028
ar.L20         0.0201      0.004      5.688      0.000       0.013       0.027
ar.L21        -0.0009      0.004     -0.236      0.813      -0.008       0.007
ar.L22        -0.0145      0.004     -3.648      0.000      -0.022      -0.007
ar.L23         0.0120      0.004      3.173      0.002       0.005       0.019
ar.L24        -0.0091      0.004     -2.418      0.016      -0.016      -0.002
ar.L25        -0.0083      0.004     -2.119      0.034      -0.016      -0.001
ar.L26         0.0263      0.004      7.064      0.000       0.019       0.034
ar.L27         0.0072      0.004      1.657      0.097      -0.001       0.016
ar.L28        -0.0037      0.003     -1.153      0.249      -0.010       0.003
ar.L29         0.0606      0.003     19.484      0.000       0.054       0.067
ar.L30         0.0447      0.006      8.015      0.000       0.034       0.056
ma.L1          0.2221      0.087      2.549      0.011       0.051       0.393
sigma2         1.5587      0.004    365.702      0.000       1.550       1.567
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           1739529.44
Prob(Q):                              0.99   Prob(JB):                         0.00
Heteroskedasticity (H):            5470.25   Skew:                             0.27
Prob(H) (two-sided):                  0.00   Kurtosis:                        57.19
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14218    339.693737
14219    339.735567
14220    339.671875
14221    339.404602
14222    339.523362
14223    339.427161
14224    339.001256
14225    338.932304
14226    339.060881
14227    338.880617
Name: predicted_mean, dtype: float64
       lower price  upper price
14218   337.246730   342.140743
14219   336.221546   343.249587
14220   335.392760   343.950991
14221   334.456130   344.353074
14222   333.990068   345.056656
14223   333.354065   345.500256
14224   332.410390   345.592122
14225   331.843372   346.021236
14226   331.509524   346.612239
14227   330.902967   346.858266

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 5.8149838303841515
In [557]:
arima_forecast('GARAN', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  50.05
2023-09-20 10:00:00+03:00  51.15
2023-09-20 11:00:00+03:00  52.25
2023-09-20 12:00:00+03:00  52.00
2023-09-20 13:00:00+03:00  52.25
2023-09-20 14:00:00+03:00  52.25
2023-09-20 15:00:00+03:00  52.45
2023-09-20 16:00:00+03:00  52.45
2023-09-20 17:00:00+03:00  51.35
2023-09-20 18:00:00+03:00  51.50
2023-09-21 09:00:00+03:00  51.50
2023-09-21 10:00:00+03:00  52.25
2023-09-21 11:00:00+03:00  52.45
2023-09-21 12:00:00+03:00  52.40
2023-09-21 13:00:00+03:00  52.90
2023-09-21 14:00:00+03:00  51.20
2023-09-21 15:00:00+03:00  51.15
2023-09-21 16:00:00+03:00  51.90
2023-09-21 17:00:00+03:00  51.85
2023-09-21 18:00:00+03:00  52.00
2023-09-22 09:00:00+03:00  52.00
2023-09-22 10:00:00+03:00  51.60
2023-09-22 11:00:00+03:00  51.75
2023-09-22 12:00:00+03:00  51.75
2023-09-22 13:00:00+03:00  52.05
2023-09-22 14:00:00+03:00  51.30
2023-09-22 15:00:00+03:00  51.25
2023-09-22 16:00:00+03:00  51.45
2023-09-22 17:00:00+03:00  50.85
2023-09-22 18:00:00+03:00  50.80
Mean of the first 10 values: price    51.44
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  50.95
2023-09-25 10:00:00+03:00  51.05
2023-09-25 11:00:00+03:00  51.20
2023-09-25 12:00:00+03:00  51.35
2023-09-25 13:00:00+03:00  51.85
2023-09-25 14:00:00+03:00  51.40
2023-09-25 15:00:00+03:00  51.40
2023-09-25 16:00:00+03:00  52.05
2023-09-25 17:00:00+03:00  51.60
2023-09-25 18:00:00+03:00  51.55
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.1110
2018-01-02 11:00:00+03:00  0.0257
2018-01-02 12:00:00+03:00 -0.0172
2018-01-02 13:00:00+03:00  0.0086
2018-01-02 14:00:00+03:00  0.0086
...                           ...
2023-09-22 14:00:00+03:00 -0.7500
2023-09-22 15:00:00+03:00 -0.0500
2023-09-22 16:00:00+03:00  0.2000
2023-09-22 17:00:00+03:00 -0.6000
2023-09-22 18:00:00+03:00 -0.0500

[14226 rows x 1 columns]
ADF Statistic: -18.961120996132177
p-value: 0.0
Critical Values: {'1%': -3.4308111170220643, '5%': -2.8617437929148606, '10%': -2.566878474199101}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood                4789.822
Date:                Sun, 24 Dec 2023   AIC                          -9515.644
Time:                        01:34:19   BIC                          -9273.633
Sample:                             0   HQIC                         -9435.139
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.0050      5.320     -0.001      0.999     -10.432      10.422
ar.L2          0.0155      0.055      0.279      0.780      -0.093       0.124
ar.L3          0.0290      0.082      0.354      0.723      -0.132       0.190
ar.L4         -0.0075      0.155     -0.048      0.961      -0.311       0.296
ar.L5          0.0275      0.039      0.697      0.486      -0.050       0.105
ar.L6         -0.0057      0.146     -0.039      0.969      -0.292       0.281
ar.L7          0.0050      0.030      0.168      0.867      -0.053       0.063
ar.L8          0.0001      0.027      0.005      0.996      -0.052       0.052
ar.L9          0.0336      0.004      9.116      0.000       0.026       0.041
ar.L10         0.0279      0.178      0.157      0.876      -0.322       0.378
ar.L11        -0.0066      0.150     -0.044      0.965      -0.300       0.287
ar.L12        -0.0150      0.035     -0.433      0.665      -0.083       0.053
ar.L13        -0.0082      0.079     -0.103      0.918      -0.164       0.148
ar.L14        -0.0015      0.044     -0.033      0.973      -0.088       0.085
ar.L15         0.0064      0.009      0.732      0.464      -0.011       0.023
ar.L16        -0.0106      0.034     -0.317      0.751      -0.076       0.055
ar.L17         0.0472      0.056      0.841      0.400      -0.063       0.157
ar.L18        -0.0039      0.251     -0.015      0.988      -0.496       0.488
ar.L19         0.0219      0.019      1.128      0.259      -0.016       0.060
ar.L20        -0.0191      0.116     -0.164      0.870      -0.247       0.209
ar.L21        -0.0100      0.101     -0.098      0.922      -0.209       0.189
ar.L22         0.0014      0.054      0.027      0.979      -0.104       0.107
ar.L23         0.0038      0.008      0.459      0.646      -0.012       0.020
ar.L24        -0.0173      0.021     -0.819      0.413      -0.059       0.024
ar.L25        -0.0054      0.092     -0.059      0.953      -0.186       0.175
ar.L26         0.0148      0.030      0.501      0.617      -0.043       0.073
ar.L27         0.0007      0.079      0.009      0.993      -0.154       0.155
ar.L28        -0.0197      0.005     -3.689      0.000      -0.030      -0.009
ar.L29         0.0027      0.105      0.026      0.979      -0.203       0.208
ar.L30        -0.0007      0.015     -0.047      0.962      -0.029       0.028
ma.L1         -0.0054      5.320     -0.001      0.999     -10.432      10.421
sigma2         0.0299   7.83e-05    381.168      0.000       0.030       0.030
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           1535050.02
Prob(Q):                              0.98   Prob(JB):                         0.00
Heteroskedasticity (H):              25.71   Skew:                             0.06
Prob(H) (two-sided):                  0.00   Kurtosis:                        53.89
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    50.730948
14228    50.740895
14229    50.677426
14230    50.684403
14231    50.644166
14232    50.621974
14233    50.681738
14234    50.666349
14235    50.620367
14236    50.639842
Name: predicted_mean, dtype: float64
       lower price  upper price
14227    50.392279    51.069616
14228    50.264428    51.217362
14229    50.091851    51.263000
14230    50.002124    51.366683
14231    49.878471    51.409861
14232    49.777059    51.466890
14233    49.765147    51.598328
14234    49.682620    51.650077
14235    49.573618    51.667117
14236    49.529996    51.749687

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.8448343909255085
In [558]:
arima_forecast('KRDMD', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  26.00
2023-09-20 10:00:00+03:00  26.12
2023-09-20 11:00:00+03:00  25.82
2023-09-20 12:00:00+03:00  26.06
2023-09-20 13:00:00+03:00  26.02
2023-09-20 14:00:00+03:00  26.20
2023-09-20 15:00:00+03:00  26.12
2023-09-20 16:00:00+03:00  25.68
2023-09-20 17:00:00+03:00  25.58
2023-09-20 18:00:00+03:00  25.60
2023-09-21 09:00:00+03:00  25.50
2023-09-21 10:00:00+03:00  25.36
2023-09-21 11:00:00+03:00  25.46
2023-09-21 12:00:00+03:00  25.34
2023-09-21 13:00:00+03:00  25.58
2023-09-21 14:00:00+03:00  26.06
2023-09-21 15:00:00+03:00  25.96
2023-09-21 16:00:00+03:00  26.22
2023-09-21 17:00:00+03:00  26.44
2023-09-21 18:00:00+03:00  26.46
2023-09-22 09:00:00+03:00  26.40
2023-09-22 10:00:00+03:00  26.60
2023-09-22 11:00:00+03:00  26.78
2023-09-22 12:00:00+03:00  26.54
2023-09-22 13:00:00+03:00  26.50
2023-09-22 14:00:00+03:00  27.32
2023-09-22 15:00:00+03:00  27.40
2023-09-22 16:00:00+03:00  27.56
2023-09-22 17:00:00+03:00  27.62
2023-09-22 18:00:00+03:00  27.52
Mean of the first 10 values: price    29.704
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  27.64
2023-09-25 10:00:00+03:00  28.60
2023-09-25 11:00:00+03:00  29.48
2023-09-25 12:00:00+03:00  29.76
2023-09-25 13:00:00+03:00  30.26
2023-09-25 14:00:00+03:00  30.26
2023-09-25 15:00:00+03:00  30.26
2023-09-25 16:00:00+03:00  30.26
2023-09-25 17:00:00+03:00  30.26
2023-09-25 18:00:00+03:00  30.26
                            price
timestamp                        
2018-01-02 10:00:00+03:00 -0.0166
2018-01-02 11:00:00+03:00  0.0415
2018-01-02 12:00:00+03:00 -0.0249
2018-01-02 13:00:00+03:00  0.0000
2018-01-02 14:00:00+03:00  0.0083
...                           ...
2023-09-22 14:00:00+03:00  0.8200
2023-09-22 15:00:00+03:00  0.0800
2023-09-22 16:00:00+03:00  0.1600
2023-09-22 17:00:00+03:00  0.0600
2023-09-22 18:00:00+03:00 -0.1000

[14226 rows x 1 columns]
ADF Statistic: -17.01210552725342
p-value: 8.576566083830768e-30
Critical Values: {'1%': -3.430811052000141, '5%': -2.8617437641803356, '10%': -2.566878458903999}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood               11905.061
Date:                Sun, 24 Dec 2023   AIC                         -23746.122
Time:                        01:36:15   BIC                         -23504.111
Sample:                             0   HQIC                        -23665.617
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.4050      0.200      2.024      0.043       0.013       0.797
ar.L2         -0.0285      0.011     -2.540      0.011      -0.050      -0.007
ar.L3          0.0454      0.005      9.350      0.000       0.036       0.055
ar.L4         -0.0059      0.010     -0.615      0.538      -0.025       0.013
ar.L5         -0.0212      0.004     -4.941      0.000      -0.030      -0.013
ar.L6          0.0077      0.006      1.373      0.170      -0.003       0.019
ar.L7          0.0015      0.004      0.344      0.731      -0.007       0.010
ar.L8         -0.0057      0.005     -1.170      0.242      -0.015       0.004
ar.L9          0.0429      0.005      9.308      0.000       0.034       0.052
ar.L10        -0.0331      0.009     -3.578      0.000      -0.051      -0.015
ar.L11        -0.0321      0.006     -5.311      0.000      -0.044      -0.020
ar.L12         0.0061      0.009      0.682      0.495      -0.011       0.024
ar.L13         0.0089      0.005      1.795      0.073      -0.001       0.019
ar.L14         0.0124      0.005      2.541      0.011       0.003       0.022
ar.L15        -0.0256      0.005     -5.203      0.000      -0.035      -0.016
ar.L16         0.0176      0.006      3.082      0.002       0.006       0.029
ar.L17        -0.0132      0.005     -2.636      0.008      -0.023      -0.003
ar.L18         0.0053      0.005      1.038      0.299      -0.005       0.015
ar.L19         0.0417      0.005      8.202      0.000       0.032       0.052
ar.L20        -0.0090      0.009     -0.995      0.320      -0.027       0.009
ar.L21        -0.0076      0.004     -1.777      0.076      -0.016       0.001
ar.L22         0.0039      0.005      0.782      0.434      -0.006       0.014
ar.L23        -0.0025      0.005     -0.542      0.588      -0.012       0.007
ar.L24      4.837e-05      0.005      0.010      0.992      -0.010       0.010
ar.L25         0.0279      0.005      6.025      0.000       0.019       0.037
ar.L26        -0.0360      0.006     -5.769      0.000      -0.048      -0.024
ar.L27         0.0069      0.007      0.995      0.320      -0.007       0.020
ar.L28        -0.0190      0.004     -4.313      0.000      -0.028      -0.010
ar.L29         0.0243      0.006      3.980      0.000       0.012       0.036
ar.L30         0.0098      0.006      1.615      0.106      -0.002       0.022
ma.L1         -0.3529      0.200     -1.763      0.078      -0.745       0.039
sigma2         0.0110   3.28e-05    334.227      0.000       0.011       0.011
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           1221518.35
Prob(Q):                              0.97   Prob(JB):                         0.00
Heteroskedasticity (H):              45.46   Skew:                             0.57
Prob(H) (two-sided):                  0.00   Kurtosis:                        48.38
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    27.513486
14228    27.523761
14229    27.478515
14230    27.480302
14231    27.557107
14232    27.558100
14233    27.527989
14234    27.525918
14235    27.516256
14236    27.526801
Name: predicted_mean, dtype: float64
       lower price  upper price
14227    27.308117    27.718855
14228    27.225657    27.821865
14229    27.111225    27.845805
14230    27.050637    27.909966
14231    27.071781    28.042433
14232    27.024385    28.091815
14233    26.949845    28.106133
14234    26.906282    28.145554
14235    26.858161    28.174351
14236    26.829692    28.223910

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 2.343517765674792
In [639]:
arima_forecast('KCHOL', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  134.8
2023-09-20 10:00:00+03:00  136.9
2023-09-20 11:00:00+03:00  136.4
2023-09-20 12:00:00+03:00  136.4
2023-09-20 13:00:00+03:00  136.8
2023-09-20 14:00:00+03:00  136.5
2023-09-20 15:00:00+03:00  136.0
2023-09-20 16:00:00+03:00  135.9
2023-09-20 17:00:00+03:00  134.2
2023-09-20 18:00:00+03:00  134.6
2023-09-21 09:00:00+03:00  134.1
2023-09-21 10:00:00+03:00  133.7
2023-09-21 11:00:00+03:00  133.7
2023-09-21 12:00:00+03:00  133.7
2023-09-21 13:00:00+03:00  134.5
2023-09-21 14:00:00+03:00  136.9
2023-09-21 15:00:00+03:00  137.0
2023-09-21 16:00:00+03:00  136.9
2023-09-21 17:00:00+03:00  138.7
2023-09-21 18:00:00+03:00  138.8
2023-09-22 09:00:00+03:00  139.3
2023-09-22 10:00:00+03:00  140.6
2023-09-22 11:00:00+03:00  139.3
2023-09-22 12:00:00+03:00  140.0
2023-09-22 13:00:00+03:00  139.8
2023-09-22 14:00:00+03:00  138.8
2023-09-22 15:00:00+03:00  138.9
2023-09-22 16:00:00+03:00  139.0
2023-09-22 17:00:00+03:00  137.3
2023-09-22 18:00:00+03:00  137.5
Mean of the first 10 values: price    141.23
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  138.0
2023-09-25 10:00:00+03:00  139.8
2023-09-25 11:00:00+03:00  139.9
2023-09-25 12:00:00+03:00  141.2
2023-09-25 13:00:00+03:00  141.9
2023-09-25 14:00:00+03:00  142.2
2023-09-25 15:00:00+03:00  142.2
2023-09-25 16:00:00+03:00  142.8
2023-09-25 17:00:00+03:00  142.3
2023-09-25 18:00:00+03:00  142.0
                            price
timestamp                        
2018-01-02 10:00:00+03:00 -0.0262
2018-01-02 11:00:00+03:00  0.2524
2018-01-02 12:00:00+03:00 -0.0870
2018-01-02 13:00:00+03:00  0.0782
2018-01-02 14:00:00+03:00  0.1305
...                           ...
2023-09-22 14:00:00+03:00 -1.0000
2023-09-22 15:00:00+03:00  0.1000
2023-09-22 16:00:00+03:00  0.1000
2023-09-22 17:00:00+03:00 -1.7000
2023-09-22 18:00:00+03:00  0.2000

[14226 rows x 1 columns]
ADF Statistic: -18.49540065440885
p-value: 2.124049178894633e-30
Critical Values: {'1%': -3.4308111170220643, '5%': -2.8617437929148606, '10%': -2.566878474199101}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood               -6813.630
Date:                Sun, 24 Dec 2023   AIC                          13691.261
Time:                        16:27:47   BIC                          13933.271
Sample:                             0   HQIC                         13771.765
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.8445      0.043    -19.504      0.000      -0.929      -0.760
ar.L2         -0.0314      0.006     -5.666      0.000      -0.042      -0.021
ar.L3          0.0318      0.005      6.110      0.000       0.022       0.042
ar.L4          0.0090      0.005      1.849      0.064      -0.001       0.018
ar.L5         -0.0003      0.005     -0.064      0.949      -0.009       0.009
ar.L6          0.0618      0.005     12.981      0.000       0.052       0.071
ar.L7          0.0164      0.006      2.891      0.004       0.005       0.027
ar.L8         -0.0032      0.006     -0.555      0.579      -0.015       0.008
ar.L9          0.0339      0.005      7.059      0.000       0.024       0.043
ar.L10         0.0209      0.004      5.378      0.000       0.013       0.029
ar.L11        -0.0268      0.004     -6.769      0.000      -0.035      -0.019
ar.L12        -0.0271      0.005     -5.668      0.000      -0.036      -0.018
ar.L13         0.0097      0.005      1.878      0.060      -0.000       0.020
ar.L14         0.0317      0.005      5.966      0.000       0.021       0.042
ar.L15         0.0030      0.005      0.599      0.549      -0.007       0.013
ar.L16        -0.0276      0.005     -5.660      0.000      -0.037      -0.018
ar.L17         0.0233      0.005      4.629      0.000       0.013       0.033
ar.L18         0.0062      0.005      1.168      0.243      -0.004       0.017
ar.L19        -0.0437      0.004    -11.102      0.000      -0.051      -0.036
ar.L20        -0.0570      0.005    -12.482      0.000      -0.066      -0.048
ar.L21        -0.0337      0.005     -6.487      0.000      -0.044      -0.023
ar.L22         0.0265      0.005      4.923      0.000       0.016       0.037
ar.L23         0.0095      0.005      1.771      0.077      -0.001       0.020
ar.L24        -0.0047      0.005     -0.882      0.378      -0.015       0.006
ar.L25         0.0250      0.005      5.182      0.000       0.016       0.034
ar.L26         0.0244      0.005      4.694      0.000       0.014       0.035
ar.L27         0.0127      0.005      2.315      0.021       0.002       0.023
ar.L28         0.0198      0.006      3.560      0.000       0.009       0.031
ar.L29         0.0126      0.005      2.712      0.007       0.003       0.022
ar.L30        -0.0176      0.004     -4.571      0.000      -0.025      -0.010
ma.L1          0.7949      0.043     18.477      0.000       0.711       0.879
sigma2         0.1526      0.000    357.999      0.000       0.152       0.153
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           2839473.97
Prob(Q):                              0.96   Prob(JB):                         0.00
Heteroskedasticity (H):              51.02   Skew:                            -0.66
Prob(H) (two-sided):                  0.00   Kurtosis:                        72.20
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    137.384395
14228    137.265869
14229    137.425405
14230    137.240580
14231    137.033361
14232    137.046422
14233    136.941715
14234    136.940253
14235    136.910920
14236    136.792110
Name: predicted_mean, dtype: float64
       lower price  upper price
14227   136.618781   138.150009
14228   136.209605   138.322133
14229   136.138182   138.712629
14230   135.748561   138.732599
14231   135.366124   138.700598
14232   135.217472   138.875371
14233   134.948030   138.935400
14234   134.803701   139.076806
14235   134.634424   139.187415
14236   134.378979   139.205241

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 4.438417034228074
In [560]:
arima_forecast('KOZAL', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  28.14
2023-09-20 10:00:00+03:00  28.70
2023-09-20 11:00:00+03:00  28.28
2023-09-20 12:00:00+03:00  28.50
2023-09-20 13:00:00+03:00  28.32
2023-09-20 14:00:00+03:00  28.18
2023-09-20 15:00:00+03:00  27.90
2023-09-20 16:00:00+03:00  27.86
2023-09-20 17:00:00+03:00  27.26
2023-09-20 18:00:00+03:00  27.34
2023-09-21 09:00:00+03:00  27.30
2023-09-21 10:00:00+03:00  26.96
2023-09-21 11:00:00+03:00  27.20
2023-09-21 12:00:00+03:00  27.04
2023-09-21 13:00:00+03:00  27.20
2023-09-21 14:00:00+03:00  27.82
2023-09-21 15:00:00+03:00  27.72
2023-09-21 16:00:00+03:00  28.00
2023-09-21 17:00:00+03:00  28.46
2023-09-21 18:00:00+03:00  28.60
2023-09-22 09:00:00+03:00  28.78
2023-09-22 10:00:00+03:00  28.46
2023-09-22 11:00:00+03:00  28.44
2023-09-22 12:00:00+03:00  28.30
2023-09-22 13:00:00+03:00  28.26
2023-09-22 14:00:00+03:00  28.12
2023-09-22 15:00:00+03:00  28.04
2023-09-22 16:00:00+03:00  28.04
2023-09-22 17:00:00+03:00  27.94
2023-09-22 18:00:00+03:00  28.02
Mean of the first 10 values: price    28.638
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  28.22
2023-09-25 10:00:00+03:00  28.38
2023-09-25 11:00:00+03:00  28.54
2023-09-25 12:00:00+03:00  28.58
2023-09-25 13:00:00+03:00  28.60
2023-09-25 14:00:00+03:00  28.68
2023-09-25 15:00:00+03:00  28.54
2023-09-25 16:00:00+03:00  28.88
2023-09-25 17:00:00+03:00  28.96
2023-09-25 18:00:00+03:00  29.00
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.0204
2018-01-02 11:00:00+03:00 -0.0051
2018-01-02 12:00:00+03:00 -0.0034
2018-01-02 13:00:00+03:00  0.0000
2018-01-02 14:00:00+03:00  0.0144
...                           ...
2023-09-22 14:00:00+03:00 -0.1400
2023-09-22 15:00:00+03:00 -0.0800
2023-09-22 16:00:00+03:00  0.0000
2023-09-22 17:00:00+03:00 -0.1000
2023-09-22 18:00:00+03:00  0.0800

[14226 rows x 1 columns]
ADF Statistic: -17.534053619974674
p-value: 4.222304233206124e-30
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood                9132.750
Date:                Sun, 24 Dec 2023   AIC                         -18201.499
Time:                        01:40:06   BIC                         -17959.489
Sample:                             0   HQIC                        -18120.995
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.2621      0.125     -2.094      0.036      -0.508      -0.017
ar.L2         -0.0311      0.005     -6.617      0.000      -0.040      -0.022
ar.L3         -0.0376      0.006     -6.485      0.000      -0.049      -0.026
ar.L4         -0.0009      0.005     -0.194      0.847      -0.010       0.008
ar.L5          0.0149      0.003      4.271      0.000       0.008       0.022
ar.L6          0.0105      0.004      2.803      0.005       0.003       0.018
ar.L7          0.0735      0.003     22.574      0.000       0.067       0.080
ar.L8          0.0304      0.010      3.052      0.002       0.011       0.050
ar.L9          0.0473      0.003     13.623      0.000       0.040       0.054
ar.L10        -0.0059      0.006     -1.021      0.307      -0.017       0.005
ar.L11        -0.0405      0.004    -11.477      0.000      -0.047      -0.034
ar.L12        -0.0599      0.005    -10.969      0.000      -0.071      -0.049
ar.L13        -0.0226      0.007     -3.156      0.002      -0.037      -0.009
ar.L14         0.0265      0.004      6.707      0.000       0.019       0.034
ar.L15         0.0039      0.005      0.751      0.453      -0.006       0.014
ar.L16         0.0162      0.003      5.031      0.000       0.010       0.023
ar.L17         0.0030      0.004      0.751      0.453      -0.005       0.011
ar.L18         0.0103      0.004      2.558      0.011       0.002       0.018
ar.L19         0.0472      0.004     11.241      0.000       0.039       0.055
ar.L20         0.0452      0.006      7.165      0.000       0.033       0.058
ar.L21        -0.0332      0.005     -6.443      0.000      -0.043      -0.023
ar.L22         0.0006      0.006      0.091      0.928      -0.012       0.013
ar.L23        -0.0174      0.004     -4.551      0.000      -0.025      -0.010
ar.L24         0.0179      0.004      4.500      0.000       0.010       0.026
ar.L25         0.0121      0.005      2.538      0.011       0.003       0.021
ar.L26        -0.0311      0.003     -9.045      0.000      -0.038      -0.024
ar.L27        -0.0362      0.005     -6.816      0.000      -0.047      -0.026
ar.L28        -0.0546      0.005    -10.916      0.000      -0.064      -0.045
ar.L29        -0.0219      0.007     -3.271      0.001      -0.035      -0.009
ar.L30         0.0233      0.004      6.588      0.000       0.016       0.030
ma.L1          0.2824      0.125      2.262      0.024       0.038       0.527
sigma2         0.0162   4.26e-05    380.429      0.000       0.016       0.016
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           1541041.44
Prob(Q):                              0.99   Prob(JB):                         0.00
Heteroskedasticity (H):             156.40   Skew:                             1.34
Prob(H) (two-sided):                  0.00   Kurtosis:                        53.92
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    27.948406
14228    27.974134
14229    27.972966
14230    28.009500
14231    28.099376
14232    28.104799
14233    28.136263
14234    28.177788
14235    28.176670
14236    28.201441
Name: predicted_mean, dtype: float64
       lower price  upper price
14227    27.698842    28.197971
14228    27.617608    28.330660
14229    27.540073    28.405859
14230    27.515311    28.503689
14231    27.549940    28.648813
14232    27.503644    28.705955
14233    27.486775    28.785751
14234    27.476909    28.878667
14235    27.426876    28.926465
14236    27.402382    29.000500

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.5808936652885004
In [561]:
arima_forecast('KOZAA', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  63.60
2023-09-20 10:00:00+03:00  65.40
2023-09-20 11:00:00+03:00  64.65
2023-09-20 12:00:00+03:00  66.45
2023-09-20 13:00:00+03:00  65.90
2023-09-20 14:00:00+03:00  65.65
2023-09-20 15:00:00+03:00  64.25
2023-09-20 16:00:00+03:00  64.30
2023-09-20 17:00:00+03:00  64.30
2023-09-20 18:00:00+03:00  64.10
2023-09-21 09:00:00+03:00  63.95
2023-09-21 10:00:00+03:00  63.25
2023-09-21 11:00:00+03:00  63.90
2023-09-21 12:00:00+03:00  63.65
2023-09-21 13:00:00+03:00  63.55
2023-09-21 14:00:00+03:00  65.25
2023-09-21 15:00:00+03:00  65.05
2023-09-21 16:00:00+03:00  65.85
2023-09-21 17:00:00+03:00  66.75
2023-09-21 18:00:00+03:00  66.65
2023-09-22 09:00:00+03:00  65.80
2023-09-22 10:00:00+03:00  65.90
2023-09-22 11:00:00+03:00  66.00
2023-09-22 12:00:00+03:00  65.40
2023-09-22 13:00:00+03:00  65.20
2023-09-22 14:00:00+03:00  64.60
2023-09-22 15:00:00+03:00  64.60
2023-09-22 16:00:00+03:00  64.45
2023-09-22 17:00:00+03:00  64.15
2023-09-22 18:00:00+03:00  64.45
Mean of the first 10 values: price    65.285
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  65.30
2023-09-25 10:00:00+03:00  65.15
2023-09-25 11:00:00+03:00  65.00
2023-09-25 12:00:00+03:00  65.20
2023-09-25 13:00:00+03:00  65.55
2023-09-25 14:00:00+03:00  65.60
2023-09-25 15:00:00+03:00  64.75
2023-09-25 16:00:00+03:00  65.25
2023-09-25 17:00:00+03:00  65.50
2023-09-25 18:00:00+03:00  65.55
                           price
timestamp                       
2018-01-02 10:00:00+03:00   0.02
2018-01-02 11:00:00+03:00  -0.03
2018-01-02 12:00:00+03:00  -0.02
2018-01-02 13:00:00+03:00   0.00
2018-01-02 14:00:00+03:00   0.02
...                          ...
2023-09-22 14:00:00+03:00  -0.60
2023-09-22 15:00:00+03:00   0.00
2023-09-22 16:00:00+03:00  -0.15
2023-09-22 17:00:00+03:00  -0.30
2023-09-22 18:00:00+03:00   0.30

[14226 rows x 1 columns]
ADF Statistic: -15.864295639873056
p-value: 9.135692280899592e-29
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood               -2559.492
Date:                Sun, 24 Dec 2023   AIC                           5182.985
Time:                        01:40:35   BIC                           5424.995
Sample:                             0   HQIC                          5263.489
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.0016      0.398      0.004      0.997      -0.778       0.781
ar.L2          0.0048      0.005      1.039      0.299      -0.004       0.014
ar.L3         -0.0289      0.004     -7.836      0.000      -0.036      -0.022
ar.L4          0.0048      0.012      0.417      0.676      -0.018       0.027
ar.L5         -0.0204      0.004     -5.038      0.000      -0.028      -0.012
ar.L6          0.0173      0.009      1.968      0.049    7.07e-05       0.034
ar.L7          0.0570      0.008      7.105      0.000       0.041       0.073
ar.L8         -0.0081      0.023     -0.348      0.728      -0.054       0.038
ar.L9          0.0282      0.005      5.857      0.000       0.019       0.038
ar.L10        -0.0118      0.012     -1.029      0.304      -0.034       0.011
ar.L11        -0.0368      0.005     -6.756      0.000      -0.048      -0.026
ar.L12        -0.0221      0.015     -1.468      0.142      -0.052       0.007
ar.L13        -0.0308      0.010     -3.219      0.001      -0.050      -0.012
ar.L14         0.0225      0.013      1.777      0.076      -0.002       0.047
ar.L15         0.0036      0.010      0.355      0.723      -0.016       0.024
ar.L16         0.0054      0.004      1.337      0.181      -0.003       0.013
ar.L17        -0.0099      0.005     -2.161      0.031      -0.019      -0.001
ar.L18         0.0139      0.006      2.262      0.024       0.002       0.026
ar.L19         0.0415      0.007      5.923      0.000       0.028       0.055
ar.L20         0.0484      0.017      2.901      0.004       0.016       0.081
ar.L21        -0.0161      0.019     -0.828      0.407      -0.054       0.022
ar.L22         0.0030      0.007      0.406      0.685      -0.012       0.018
ar.L23        -0.0068      0.004     -1.758      0.079      -0.014       0.001
ar.L24         0.0219      0.005      4.722      0.000       0.013       0.031
ar.L25         0.0149      0.010      1.458      0.145      -0.005       0.035
ar.L26        -0.0189      0.007     -2.647      0.008      -0.033      -0.005
ar.L27         0.0031      0.009      0.369      0.712      -0.014       0.020
ar.L28        -0.0297      0.004     -6.835      0.000      -0.038      -0.021
ar.L29         0.0377      0.013      2.992      0.003       0.013       0.062
ar.L30         0.0096      0.016      0.621      0.535      -0.021       0.040
ma.L1          0.0017      0.397      0.004      0.997      -0.777       0.780
sigma2         0.0839      0.000    326.465      0.000       0.083       0.084
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):            687694.64
Prob(Q):                              0.98   Prob(JB):                         0.00
Heteroskedasticity (H):              53.01   Skew:                             1.25
Prob(H) (two-sided):                  0.00   Kurtosis:                        36.97
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    64.454724
14228    64.392487
14229    64.493224
14230    64.522109
14231    64.578713
14232    64.590244
14233    64.655377
14234    64.764319
14235    64.770127
14236    64.773252
Name: predicted_mean, dtype: float64
       lower price  upper price
14227    63.887000    65.022447
14228    63.588302    65.196672
14229    63.506191    65.480257
14230    63.389307    65.654912
14231    63.315695    65.841732
14232    63.213929    65.966560
14233    63.170595    66.140158
14234    63.166898    66.361740
14235    63.068768    66.471486
14236    62.968811    66.577693

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.7313392743337747
In [569]:
arima_forecast('PGSUS', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  780.0
2023-09-20 10:00:00+03:00  778.3
2023-09-20 11:00:00+03:00  773.8
2023-09-20 12:00:00+03:00  774.6
2023-09-20 13:00:00+03:00  772.6
2023-09-20 14:00:00+03:00  773.9
2023-09-20 15:00:00+03:00  769.5
2023-09-20 16:00:00+03:00  760.7
2023-09-20 17:00:00+03:00  758.7
2023-09-20 18:00:00+03:00  755.0
2023-09-21 09:00:00+03:00  754.0
2023-09-21 10:00:00+03:00  742.9
2023-09-21 11:00:00+03:00  744.3
2023-09-21 12:00:00+03:00  742.4
2023-09-21 13:00:00+03:00  746.4
2023-09-21 14:00:00+03:00  764.3
2023-09-21 15:00:00+03:00  756.8
2023-09-21 16:00:00+03:00  763.7
2023-09-21 17:00:00+03:00  774.1
2023-09-21 18:00:00+03:00  772.8
2023-09-22 09:00:00+03:00  779.8
2023-09-22 10:00:00+03:00  769.5
2023-09-22 11:00:00+03:00  774.7
2023-09-22 12:00:00+03:00  773.2
2023-09-22 13:00:00+03:00  770.6
2023-09-22 14:00:00+03:00  762.5
2023-09-22 15:00:00+03:00  761.6
2023-09-22 16:00:00+03:00  761.4
2023-09-22 17:00:00+03:00  766.2
2023-09-22 18:00:00+03:00  767.2
Mean of the first 10 values: price    781.74
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  770.0
2023-09-25 10:00:00+03:00  763.0
2023-09-25 11:00:00+03:00  772.6
2023-09-25 12:00:00+03:00  781.6
2023-09-25 13:00:00+03:00  782.8
2023-09-25 14:00:00+03:00  778.9
2023-09-25 15:00:00+03:00  780.1
2023-09-25 16:00:00+03:00  787.4
2023-09-25 17:00:00+03:00  801.0
2023-09-25 18:00:00+03:00  800.0
                           price
timestamp                       
2018-01-02 10:00:00+03:00   0.30
2018-01-02 11:00:00+03:00   0.74
2018-01-02 12:00:00+03:00   0.08
2018-01-02 13:00:00+03:00  -0.02
2018-01-02 14:00:00+03:00   0.20
...                          ...
2023-09-22 14:00:00+03:00  -8.10
2023-09-22 15:00:00+03:00  -0.90
2023-09-22 16:00:00+03:00  -0.20
2023-09-22 17:00:00+03:00   4.80
2023-09-22 18:00:00+03:00   1.00

[14221 rows x 1 columns]
ADF Statistic: -17.44002637430114
p-value: 4.7149999043698234e-30
Critical Values: {'1%': -3.4308113121979202, '5%': -2.861743879167081, '10%': -2.5668785201103015}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14222
Model:                ARIMA(30, 1, 1)   Log Likelihood              -32701.645
Date:                Sun, 24 Dec 2023   AIC                          65467.290
Time:                        01:53:59   BIC                          65709.289
Sample:                             0   HQIC                         65547.792
                              - 14222                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.4456      0.093      4.789      0.000       0.263       0.628
ar.L2          0.0023      0.006      0.399      0.690      -0.009       0.014
ar.L3          0.0205      0.004      5.066      0.000       0.013       0.028
ar.L4          0.0384      0.003     11.151      0.000       0.032       0.045
ar.L5         -0.0165      0.005     -3.224      0.001      -0.026      -0.006
ar.L6          0.0563      0.003     16.614      0.000       0.050       0.063
ar.L7         -0.0174      0.007     -2.564      0.010      -0.031      -0.004
ar.L8         -0.0256      0.004     -5.687      0.000      -0.034      -0.017
ar.L9          0.0266      0.005      5.824      0.000       0.018       0.036
ar.L10         0.0157      0.004      4.469      0.000       0.009       0.023
ar.L11        -0.0257      0.003     -7.710      0.000      -0.032      -0.019
ar.L12        -0.0241      0.004     -5.831      0.000      -0.032      -0.016
ar.L13         0.0198      0.005      3.864      0.000       0.010       0.030
ar.L14         0.0104      0.003      3.049      0.002       0.004       0.017
ar.L15        -0.0131      0.004     -3.701      0.000      -0.020      -0.006
ar.L16         0.0102      0.004      2.669      0.008       0.003       0.018
ar.L17         0.0083      0.004      2.187      0.029       0.001       0.016
ar.L18        -0.0257      0.004     -5.852      0.000      -0.034      -0.017
ar.L19         0.0143      0.004      3.700      0.000       0.007       0.022
ar.L20        -0.0275      0.003     -9.322      0.000      -0.033      -0.022
ar.L21         0.0017      0.004      0.416      0.677      -0.006       0.010
ar.L22        -0.0196      0.005     -4.288      0.000      -0.029      -0.011
ar.L23        -0.0042      0.005     -0.876      0.381      -0.014       0.005
ar.L24        -0.0088      0.004     -2.137      0.033      -0.017      -0.001
ar.L25         0.0255      0.004      5.894      0.000       0.017       0.034
ar.L26        -0.0038      0.004     -0.897      0.369      -0.012       0.004
ar.L27         0.0263      0.004      6.696      0.000       0.019       0.034
ar.L28        -0.0050      0.005     -1.019      0.308      -0.015       0.005
ar.L29         0.0148      0.003      4.286      0.000       0.008       0.022
ar.L30         0.0156      0.004      4.398      0.000       0.009       0.023
ma.L1         -0.4935      0.093     -5.325      0.000      -0.675      -0.312
sigma2         5.8193      0.013    447.311      0.000       5.794       5.845
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           3752783.47
Prob(Q):                              0.97   Prob(JB):                         0.00
Heteroskedasticity (H):             118.53   Skew:                             2.51
Prob(H) (two-sided):                  0.00   Kurtosis:                        82.42
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14222    767.121007
14223    767.504803
14224    767.888411
14225    767.996863
14226    768.095480
14227    767.365248
14228    766.795915
14229    766.230573
14230    765.045697
14231    764.605613
Name: predicted_mean, dtype: float64
       lower price  upper price
14222   762.392948   771.849066
14223   760.976566   774.033039
14224   760.009435   775.767387
14225   758.939458   777.054269
14226   757.905314   778.285646
14227   756.155516   778.574980
14228   754.546075   779.045755
14229   753.010061   779.451085
14230   750.955968   779.135426
14231   749.666317   779.544908

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 19.37967373109998
In [570]:
arima_forecast('PETKM', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  18.74
2023-09-20 10:00:00+03:00  18.87
2023-09-20 11:00:00+03:00  18.68
2023-09-20 12:00:00+03:00  18.68
2023-09-20 13:00:00+03:00  18.70
2023-09-20 14:00:00+03:00  18.67
2023-09-20 15:00:00+03:00  19.35
2023-09-20 16:00:00+03:00  19.25
2023-09-20 17:00:00+03:00  19.03
2023-09-20 18:00:00+03:00  18.92
2023-09-21 09:00:00+03:00  18.90
2023-09-21 10:00:00+03:00  18.62
2023-09-21 11:00:00+03:00  18.70
2023-09-21 12:00:00+03:00  18.65
2023-09-21 13:00:00+03:00  18.74
2023-09-21 14:00:00+03:00  19.18
2023-09-21 15:00:00+03:00  19.14
2023-09-21 16:00:00+03:00  19.43
2023-09-21 17:00:00+03:00  19.69
2023-09-21 18:00:00+03:00  19.69
2023-09-22 09:00:00+03:00  19.70
2023-09-22 10:00:00+03:00  20.08
2023-09-22 11:00:00+03:00  20.16
2023-09-22 12:00:00+03:00  20.04
2023-09-22 13:00:00+03:00  19.91
2023-09-22 14:00:00+03:00  19.75
2023-09-22 15:00:00+03:00  19.77
2023-09-22 16:00:00+03:00  19.80
2023-09-22 17:00:00+03:00  19.95
2023-09-22 18:00:00+03:00  19.90
Mean of the first 10 values: price    20.306
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  20.10
2023-09-25 10:00:00+03:00  20.22
2023-09-25 11:00:00+03:00  20.26
2023-09-25 12:00:00+03:00  20.26
2023-09-25 13:00:00+03:00  20.40
2023-09-25 14:00:00+03:00  20.30
2023-09-25 15:00:00+03:00  20.34
2023-09-25 16:00:00+03:00  20.40
2023-09-25 17:00:00+03:00  20.40
2023-09-25 18:00:00+03:00  20.38
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.0338
2018-01-02 11:00:00+03:00  0.0112
2018-01-02 12:00:00+03:00  0.0056
2018-01-02 13:00:00+03:00  0.0056
2018-01-02 14:00:00+03:00  0.0282
...                           ...
2023-09-22 14:00:00+03:00 -0.1600
2023-09-22 15:00:00+03:00  0.0200
2023-09-22 16:00:00+03:00  0.0300
2023-09-22 17:00:00+03:00  0.1500
2023-09-22 18:00:00+03:00 -0.0500

[14226 rows x 1 columns]
ADF Statistic: -16.614029542524218
p-value: 1.720907954011102e-29
Critical Values: {'1%': -3.4308111170220643, '5%': -2.8617437929148606, '10%': -2.566878474199101}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood               16627.960
Date:                Sun, 24 Dec 2023   AIC                         -33191.921
Time:                        01:56:15   BIC                         -32949.910
Sample:                             0   HQIC                        -33111.416
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.4649      0.172      2.696      0.007       0.127       0.803
ar.L2         -0.0425      0.012     -3.563      0.000      -0.066      -0.019
ar.L3          0.0339      0.005      6.543      0.000       0.024       0.044
ar.L4         -0.0011      0.006     -0.179      0.858      -0.014       0.011
ar.L5          0.0092      0.005      2.033      0.042       0.000       0.018
ar.L6          0.0250      0.005      5.111      0.000       0.015       0.035
ar.L7         -0.0262      0.007     -3.567      0.000      -0.041      -0.012
ar.L8         -0.0184      0.005     -3.402      0.001      -0.029      -0.008
ar.L9          0.0606      0.006      9.998      0.000       0.049       0.073
ar.L10        -0.0591      0.009     -6.236      0.000      -0.078      -0.041
ar.L11         0.0094      0.008      1.208      0.227      -0.006       0.025
ar.L12        -0.0506      0.005     -9.936      0.000      -0.061      -0.041
ar.L13         0.0177      0.010      1.692      0.091      -0.003       0.038
ar.L14         0.0020      0.005      0.416      0.678      -0.007       0.011
ar.L15        -0.0181      0.005     -3.807      0.000      -0.027      -0.009
ar.L16         0.0386      0.006      6.879      0.000       0.028       0.050
ar.L17         0.0003      0.007      0.035      0.972      -0.014       0.014
ar.L18        -0.0104      0.006     -1.816      0.069      -0.022       0.001
ar.L19         0.0025      0.005      0.476      0.634      -0.008       0.013
ar.L20         0.0483      0.004     10.769      0.000       0.040       0.057
ar.L21        -0.0144      0.009     -1.533      0.125      -0.033       0.004
ar.L22        -0.0037      0.005     -0.724      0.469      -0.014       0.006
ar.L23        -0.0160      0.005     -3.190      0.001      -0.026      -0.006
ar.L24         0.0081      0.006      1.353      0.176      -0.004       0.020
ar.L25        -0.0188      0.005     -3.773      0.000      -0.029      -0.009
ar.L26         0.0065      0.006      1.084      0.278      -0.005       0.018
ar.L27        -0.0170      0.005     -3.303      0.001      -0.027      -0.007
ar.L28        -0.0088      0.006     -1.424      0.155      -0.021       0.003
ar.L29         0.0204      0.006      3.601      0.000       0.009       0.032
ar.L30         0.0135      0.006      2.276      0.023       0.002       0.025
ma.L1         -0.3976      0.172     -2.306      0.021      -0.736      -0.060
sigma2         0.0057      2e-05    282.997      0.000       0.006       0.006
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):            365307.73
Prob(Q):                              0.97   Prob(JB):                         0.00
Heteroskedasticity (H):              26.12   Skew:                             0.08
Prob(H) (two-sided):                  0.00   Kurtosis:                        27.82
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    19.895279
14228    19.882561
14229    19.878222
14230    19.853798
14231    19.860673
14232    19.907857
14233    19.931177
14234    19.971163
14235    19.983371
14236    19.980858
Name: predicted_mean, dtype: float64
       lower price  upper price
14227    19.747928    20.042630
14228    19.667056    20.098065
14229    19.612414    20.144030
14230    19.543858    20.163739
14231    19.511223    20.210123
14232    19.522063    20.293650
14233    19.510294    20.352060
14234    19.518428    20.423897
14235    19.502179    20.464563
14236    19.470388    20.491328

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.3993884935461538
In [571]:
arima_forecast('SAHOL', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  56.05
2023-09-20 10:00:00+03:00  56.70
2023-09-20 11:00:00+03:00  57.10
2023-09-20 12:00:00+03:00  57.05
2023-09-20 13:00:00+03:00  56.85
2023-09-20 14:00:00+03:00  56.80
2023-09-20 15:00:00+03:00  56.85
2023-09-20 16:00:00+03:00  56.75
2023-09-20 17:00:00+03:00  55.75
2023-09-20 18:00:00+03:00  55.55
2023-09-21 09:00:00+03:00  55.45
2023-09-21 10:00:00+03:00  56.15
2023-09-21 11:00:00+03:00  55.70
2023-09-21 12:00:00+03:00  55.60
2023-09-21 13:00:00+03:00  55.75
2023-09-21 14:00:00+03:00  56.45
2023-09-21 15:00:00+03:00  56.45
2023-09-21 16:00:00+03:00  56.60
2023-09-21 17:00:00+03:00  56.90
2023-09-21 18:00:00+03:00  56.95
2023-09-22 09:00:00+03:00  57.40
2023-09-22 10:00:00+03:00  57.05
2023-09-22 11:00:00+03:00  56.85
2023-09-22 12:00:00+03:00  56.95
2023-09-22 13:00:00+03:00  56.95
2023-09-22 14:00:00+03:00  56.50
2023-09-22 15:00:00+03:00  56.50
2023-09-22 16:00:00+03:00  56.70
2023-09-22 17:00:00+03:00  56.40
2023-09-22 18:00:00+03:00  56.50
Mean of the first 10 values: price    57.66
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  56.75
2023-09-25 10:00:00+03:00  57.40
2023-09-25 11:00:00+03:00  57.45
2023-09-25 12:00:00+03:00  57.75
2023-09-25 13:00:00+03:00  57.90
2023-09-25 14:00:00+03:00  57.75
2023-09-25 15:00:00+03:00  57.60
2023-09-25 16:00:00+03:00  57.95
2023-09-25 17:00:00+03:00  57.95
2023-09-25 18:00:00+03:00  58.10
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.0000
2018-01-02 11:00:00+03:00  0.0630
2018-01-02 12:00:00+03:00 -0.0315
2018-01-02 13:00:00+03:00  0.0236
2018-01-02 14:00:00+03:00  0.0157
...                           ...
2023-09-22 14:00:00+03:00 -0.4500
2023-09-22 15:00:00+03:00  0.0000
2023-09-22 16:00:00+03:00  0.2000
2023-09-22 17:00:00+03:00 -0.3000
2023-09-22 18:00:00+03:00  0.1000

[14225 rows x 1 columns]
ADF Statistic: -22.28408897909576
p-value: 0.0
Critical Values: {'1%': -3.4308106947070907, '5%': -2.8617436062851787, '10%': -2.566878374857977}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14226
Model:                ARIMA(30, 1, 1)   Log Likelihood                4906.693
Date:                Sun, 24 Dec 2023   AIC                          -9749.386
Time:                        01:56:47   BIC                          -9507.378
Sample:                             0   HQIC                         -9668.882
                              - 14226                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.0155      0.934     -0.017      0.987      -1.846       1.815
ar.L2          0.0160      0.029      0.557      0.577      -0.040       0.072
ar.L3          0.0163      0.015      1.115      0.265      -0.012       0.045
ar.L4         -0.0052      0.015     -0.335      0.738      -0.035       0.025
ar.L5          0.0105      0.006      1.772      0.076      -0.001       0.022
ar.L6          0.0311      0.010      2.995      0.003       0.011       0.051
ar.L7         -0.0018      0.029     -0.060      0.952      -0.059       0.056
ar.L8         -0.0070      0.004     -1.663      0.096      -0.015       0.001
ar.L9          0.0250      0.007      3.343      0.001       0.010       0.040
ar.L10        -0.0345      0.024     -1.450      0.147      -0.081       0.012
ar.L11        -0.0265      0.032     -0.829      0.407      -0.089       0.036
ar.L12        -0.0299      0.026     -1.170      0.242      -0.080       0.020
ar.L13         0.0110      0.029      0.382      0.703      -0.045       0.067
ar.L14         0.0062      0.010      0.602      0.547      -0.014       0.026
ar.L15        -0.0326      0.007     -4.780      0.000      -0.046      -0.019
ar.L16         0.0174      0.031      0.568      0.570      -0.043       0.078
ar.L17         0.0215      0.016      1.318      0.188      -0.010       0.054
ar.L18        -0.0144      0.021     -0.695      0.487      -0.055       0.026
ar.L19        -0.0124      0.013     -0.923      0.356      -0.039       0.014
ar.L20         0.0225      0.012      1.860      0.063      -0.001       0.046
ar.L21        -0.0273      0.022     -1.265      0.206      -0.070       0.015
ar.L22         0.0090      0.026      0.349      0.727      -0.042       0.060
ar.L23        -0.0161      0.009     -1.754      0.079      -0.034       0.002
ar.L24         0.0038      0.015      0.251      0.802      -0.026       0.034
ar.L25        -0.0053      0.005     -1.032      0.302      -0.015       0.005
ar.L26         0.0050      0.006      0.811      0.418      -0.007       0.017
ar.L27         0.0225      0.006      3.687      0.000       0.011       0.034
ar.L28         0.0253      0.021      1.176      0.240      -0.017       0.067
ar.L29         0.0059      0.024      0.245      0.807      -0.042       0.054
ar.L30        -0.0044      0.007     -0.634      0.526      -0.018       0.009
ma.L1         -0.0152      0.934     -0.016      0.987      -1.846       1.815
sigma2         0.0294   8.72e-05    336.883      0.000       0.029       0.030
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           1320701.75
Prob(Q):                              0.96   Prob(JB):                         0.00
Heteroskedasticity (H):              38.71   Skew:                            -0.20
Prob(H) (two-sided):                  0.00   Kurtosis:                        50.20
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14226    56.442763
14227    56.470511
14228    56.453891
14229    56.478718
14230    56.445484
14231    56.441977
14232    56.420716
14233    56.425717
14234    56.413471
14235    56.408408
Name: predicted_mean, dtype: float64
       lower price  upper price
14226    56.106872    56.778655
14227    56.002729    56.938294
14228    55.880779    57.027002
14229    55.814196    57.143239
14230    55.701541    57.189427
14231    55.624743    57.259211
14232    55.532249    57.309183
14233    55.471688    57.379745
14234    55.398740    57.428201
14235    55.333548    57.483267

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 1.2778824501907873
In [572]:
arima_forecast('SASA', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  46.72
2023-09-20 10:00:00+03:00  46.76
2023-09-20 11:00:00+03:00  46.48
2023-09-20 12:00:00+03:00  46.30
2023-09-20 13:00:00+03:00  46.04
2023-09-20 14:00:00+03:00  45.80
2023-09-20 15:00:00+03:00  45.76
2023-09-20 16:00:00+03:00  46.18
2023-09-20 17:00:00+03:00  45.86
2023-09-20 18:00:00+03:00  45.60
2023-09-21 09:00:00+03:00  45.60
2023-09-21 10:00:00+03:00  44.78
2023-09-21 11:00:00+03:00  44.86
2023-09-21 12:00:00+03:00  44.68
2023-09-21 13:00:00+03:00  44.52
2023-09-21 14:00:00+03:00  45.64
2023-09-21 15:00:00+03:00  45.52
2023-09-21 16:00:00+03:00  45.78
2023-09-21 17:00:00+03:00  46.26
2023-09-21 18:00:00+03:00  46.30
2023-09-22 09:00:00+03:00  46.30
2023-09-22 10:00:00+03:00  46.34
2023-09-22 11:00:00+03:00  46.44
2023-09-22 12:00:00+03:00  46.20
2023-09-22 13:00:00+03:00  45.92
2023-09-22 14:00:00+03:00  45.68
2023-09-22 15:00:00+03:00  45.54
2023-09-22 16:00:00+03:00  45.56
2023-09-22 17:00:00+03:00  45.52
2023-09-22 18:00:00+03:00  45.52
Mean of the first 10 values: price    45.648
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  45.60
2023-09-25 10:00:00+03:00  45.28
2023-09-25 11:00:00+03:00  45.54
2023-09-25 12:00:00+03:00  45.66
2023-09-25 13:00:00+03:00  45.54
2023-09-25 14:00:00+03:00  45.46
2023-09-25 15:00:00+03:00  45.66
2023-09-25 16:00:00+03:00  45.92
2023-09-25 17:00:00+03:00  45.92
2023-09-25 18:00:00+03:00  45.90
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.0069
2018-01-02 11:00:00+03:00  0.0277
2018-01-02 12:00:00+03:00  0.0193
2018-01-02 13:00:00+03:00  0.0000
2018-01-02 14:00:00+03:00  0.0000
...                           ...
2023-09-22 14:00:00+03:00 -0.2400
2023-09-22 15:00:00+03:00 -0.1400
2023-09-22 16:00:00+03:00  0.0200
2023-09-22 17:00:00+03:00 -0.0400
2023-09-22 18:00:00+03:00  0.0000

[14224 rows x 1 columns]
ADF Statistic: -17.222504680246793
p-value: 6.2667834968518094e-30
Critical Values: {'1%': -3.430811214589344, '5%': -2.8617438360318466, '10%': -2.5668784971498444}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14225
Model:                ARIMA(30, 1, 1)   Log Likelihood               -3793.035
Date:                Sun, 24 Dec 2023   AIC                           7650.070
Time:                        01:57:43   BIC                           7892.076
Sample:                             0   HQIC                          7730.574
                              - 14225                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.2169      0.039     -5.577      0.000      -0.293      -0.141
ar.L2         -0.0484      0.004    -12.147      0.000      -0.056      -0.041
ar.L3         -0.0327      0.003     -9.394      0.000      -0.040      -0.026
ar.L4         -0.0014      0.004     -0.329      0.742      -0.010       0.007
ar.L5          0.0255      0.004      6.238      0.000       0.018       0.034
ar.L6          0.0449      0.004     10.302      0.000       0.036       0.053
ar.L7          0.0358      0.004      8.756      0.000       0.028       0.044
ar.L8         -0.0067      0.004     -1.657      0.098      -0.015       0.001
ar.L9          0.1274      0.002     59.183      0.000       0.123       0.132
ar.L10         0.1736      0.005     33.653      0.000       0.163       0.184
ar.L11        -0.0280      0.006     -4.454      0.000      -0.040      -0.016
ar.L12        -0.0385      0.004     -9.166      0.000      -0.047      -0.030
ar.L13        -0.0547      0.003    -16.076      0.000      -0.061      -0.048
ar.L14         0.0138      0.004      3.442      0.001       0.006       0.022
ar.L15        -0.0591      0.004    -14.645      0.000      -0.067      -0.051
ar.L16        -0.0264      0.004     -6.168      0.000      -0.035      -0.018
ar.L17         0.0246      0.003      7.383      0.000       0.018       0.031
ar.L18        -0.0178      0.005     -3.796      0.000      -0.027      -0.009
ar.L19         0.0131      0.002      5.609      0.000       0.009       0.018
ar.L20        -0.0233      0.003     -7.221      0.000      -0.030      -0.017
ar.L21        -0.0015      0.003     -0.465      0.642      -0.008       0.005
ar.L22         0.0064      0.004      1.664      0.096      -0.001       0.014
ar.L23        -0.0247      0.004     -6.288      0.000      -0.032      -0.017
ar.L24         0.0031      0.004      0.766      0.444      -0.005       0.011
ar.L25         0.0213      0.004      5.495      0.000       0.014       0.029
ar.L26        -0.0304      0.003     -8.910      0.000      -0.037      -0.024
ar.L27        -0.0192      0.004     -4.618      0.000      -0.027      -0.011
ar.L28        -0.0206      0.004     -5.735      0.000      -0.028      -0.014
ar.L29         0.0151      0.002      6.125      0.000       0.010       0.020
ar.L30        -0.0635      0.003    -23.272      0.000      -0.069      -0.058
ma.L1          0.1703      0.039      4.370      0.000       0.094       0.247
sigma2         0.0998      0.000    426.386      0.000       0.099       0.100
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):          12705513.90
Prob(Q):                              0.98   Prob(JB):                         0.00
Heteroskedasticity (H):            3054.72   Skew:                            -0.37
Prob(H) (two-sided):                  0.00   Kurtosis:                       149.41
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14225    45.376930
14226    45.394853
14227    45.431346
14228    45.274084
14229    45.270161
14230    45.217859
14231    45.240339
14232    45.262325
14233    45.262988
14234    45.306661
Name: predicted_mean, dtype: float64
       lower price  upper price
14225    44.757770    45.996091
14226    44.539417    46.250290
14227    44.405303    46.457389
14228    44.108585    46.439582
14229    43.978396    46.561926
14230    43.804550    46.631167
14231    43.706049    46.774630
14232    43.610713    46.913936
14233    43.505771    47.020204
14234    43.422938    47.190384

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.4180313755090521
In [573]:
arima_forecast('SISE', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  52.35
2023-09-20 10:00:00+03:00  52.45
2023-09-20 11:00:00+03:00  52.05
2023-09-20 12:00:00+03:00  52.25
2023-09-20 13:00:00+03:00  52.10
2023-09-20 14:00:00+03:00  52.00
2023-09-20 15:00:00+03:00  51.75
2023-09-20 16:00:00+03:00  51.60
2023-09-20 17:00:00+03:00  51.05
2023-09-20 18:00:00+03:00  51.10
2023-09-21 09:00:00+03:00  50.60
2023-09-21 10:00:00+03:00  50.50
2023-09-21 11:00:00+03:00  50.70
2023-09-21 12:00:00+03:00  50.70
2023-09-21 13:00:00+03:00  51.25
2023-09-21 14:00:00+03:00  52.40
2023-09-21 15:00:00+03:00  52.20
2023-09-21 16:00:00+03:00  52.45
2023-09-21 17:00:00+03:00  53.15
2023-09-21 18:00:00+03:00  52.95
2023-09-22 09:00:00+03:00  53.75
2023-09-22 10:00:00+03:00  53.65
2023-09-22 11:00:00+03:00  53.55
2023-09-22 12:00:00+03:00  53.55
2023-09-22 13:00:00+03:00  53.50
2023-09-22 14:00:00+03:00  52.90
2023-09-22 15:00:00+03:00  52.75
2023-09-22 16:00:00+03:00  53.30
2023-09-22 17:00:00+03:00  54.70
2023-09-22 18:00:00+03:00  54.70
Mean of the first 10 values: price    55.305
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  54.80
2023-09-25 10:00:00+03:00  55.40
2023-09-25 11:00:00+03:00  55.55
2023-09-25 12:00:00+03:00  55.40
2023-09-25 13:00:00+03:00  55.45
2023-09-25 14:00:00+03:00  55.15
2023-09-25 15:00:00+03:00  55.30
2023-09-25 16:00:00+03:00  55.55
2023-09-25 17:00:00+03:00  55.25
2023-09-25 18:00:00+03:00  55.20
                            price
timestamp                        
2018-01-02 10:00:00+03:00 -0.0513
2018-01-02 11:00:00+03:00  0.0256
2018-01-02 12:00:00+03:00  0.0000
2018-01-02 13:00:00+03:00  0.0000
2018-01-02 14:00:00+03:00  0.0257
...                           ...
2023-09-22 14:00:00+03:00 -0.6000
2023-09-22 15:00:00+03:00 -0.1500
2023-09-22 16:00:00+03:00  0.5500
2023-09-22 17:00:00+03:00  1.4000
2023-09-22 18:00:00+03:00  0.0000

[14226 rows x 1 columns]
ADF Statistic: -18.232040911642947
p-value: 2.3659845880078223e-30
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood                5099.806
Date:                Sun, 24 Dec 2023   AIC                         -10135.611
Time:                        01:59:20   BIC                          -9893.601
Sample:                             0   HQIC                        -10055.107
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.5683      0.093      6.087      0.000       0.385       0.751
ar.L2         -0.0315      0.005     -6.748      0.000      -0.041      -0.022
ar.L3          0.0277      0.005      5.800      0.000       0.018       0.037
ar.L4         -0.0127      0.005     -2.644      0.008      -0.022      -0.003
ar.L5          0.0146      0.004      3.528      0.000       0.007       0.023
ar.L6          0.0350      0.005      7.396      0.000       0.026       0.044
ar.L7         -0.0069      0.006     -1.144      0.252      -0.019       0.005
ar.L8         -0.0245      0.005     -5.404      0.000      -0.033      -0.016
ar.L9          0.0342      0.005      7.521      0.000       0.025       0.043
ar.L10        -0.0322      0.005     -6.449      0.000      -0.042      -0.022
ar.L11        -0.0311      0.005     -6.911      0.000      -0.040      -0.022
ar.L12        -0.0103      0.006     -1.724      0.085      -0.022       0.001
ar.L13         0.0206      0.005      3.777      0.000       0.010       0.031
ar.L14         0.0021      0.004      0.464      0.643      -0.007       0.011
ar.L15        -0.0327      0.004     -7.347      0.000      -0.041      -0.024
ar.L16         0.0037      0.005      0.716      0.474      -0.006       0.014
ar.L17         0.0247      0.005      4.804      0.000       0.015       0.035
ar.L18        -0.0241      0.005     -4.599      0.000      -0.034      -0.014
ar.L19         0.0156      0.005      3.182      0.001       0.006       0.025
ar.L20        -0.0052      0.004     -1.299      0.194      -0.013       0.003
ar.L21         0.0034      0.004      0.967      0.334      -0.004       0.010
ar.L22         0.0166      0.004      4.038      0.000       0.009       0.025
ar.L23        -0.0041      0.005     -0.864      0.388      -0.014       0.005
ar.L24        -0.0361      0.005     -7.121      0.000      -0.046      -0.026
ar.L25         0.0228      0.005      4.189      0.000       0.012       0.033
ar.L26        -0.0352      0.004     -8.541      0.000      -0.043      -0.027
ar.L27         0.0142      0.006      2.545      0.011       0.003       0.025
ar.L28         0.0237      0.005      4.774      0.000       0.014       0.033
ar.L29         0.0092      0.006      1.661      0.097      -0.002       0.020
ar.L30         0.0136      0.005      2.659      0.008       0.004       0.024
ma.L1         -0.5502      0.093     -5.910      0.000      -0.733      -0.368
sigma2         0.0286   8.31e-05    343.948      0.000       0.028       0.029
===================================================================================
Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):           2013750.05
Prob(Q):                              0.93   Prob(JB):                         0.00
Heteroskedasticity (H):              69.39   Skew:                             1.36
Prob(H) (two-sided):                  0.00   Kurtosis:                        61.22
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    54.596165
14228    54.570830
14229    54.558428
14230    54.564688
14231    54.666201
14232    54.664194
14233    54.658052
14234    54.745348
14235    54.639738
14236    54.512242
Name: predicted_mean, dtype: float64
       lower price  upper price
14227    54.264800    54.927530
14228    54.097936    55.043725
14229    53.981560    55.135296
14230    53.897420    55.231957
14231    53.919844    55.412557
14232    53.844778    55.483610
14233    53.766096    55.550008
14234    53.784243    55.706453
14235    53.616025    55.663452
14236    53.426746    55.597738

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.7187052462466499
In [574]:
arima_forecast('TAVHL', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  115.1
2023-09-20 10:00:00+03:00  115.1
2023-09-20 11:00:00+03:00  116.4
2023-09-20 12:00:00+03:00  117.6
2023-09-20 13:00:00+03:00  118.4
2023-09-20 14:00:00+03:00  118.8
2023-09-20 15:00:00+03:00  119.9
2023-09-20 16:00:00+03:00  119.7
2023-09-20 17:00:00+03:00  119.9
2023-09-20 18:00:00+03:00  120.0
2023-09-21 09:00:00+03:00  119.2
2023-09-21 10:00:00+03:00  116.6
2023-09-21 11:00:00+03:00  117.9
2023-09-21 12:00:00+03:00  117.3
2023-09-21 13:00:00+03:00  117.7
2023-09-21 14:00:00+03:00  121.9
2023-09-21 15:00:00+03:00  120.6
2023-09-21 16:00:00+03:00  120.7
2023-09-21 17:00:00+03:00  121.9
2023-09-21 18:00:00+03:00  122.9
2023-09-22 09:00:00+03:00  119.5
2023-09-22 10:00:00+03:00  119.0
2023-09-22 11:00:00+03:00  119.3
2023-09-22 12:00:00+03:00  119.2
2023-09-22 13:00:00+03:00  119.4
2023-09-22 14:00:00+03:00  118.7
2023-09-22 15:00:00+03:00  118.7
2023-09-22 16:00:00+03:00  118.8
2023-09-22 17:00:00+03:00  119.2
2023-09-22 18:00:00+03:00  119.2
Mean of the first 10 values: price    121.53
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  119.3
2023-09-25 10:00:00+03:00  118.9
2023-09-25 11:00:00+03:00  121.1
2023-09-25 12:00:00+03:00  121.1
2023-09-25 13:00:00+03:00  122.7
2023-09-25 14:00:00+03:00  121.9
2023-09-25 15:00:00+03:00  121.8
2023-09-25 16:00:00+03:00  121.9
2023-09-25 17:00:00+03:00  123.5
2023-09-25 18:00:00+03:00  123.1
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.1635
2018-01-02 11:00:00+03:00 -0.0654
2018-01-02 12:00:00+03:00  0.0490
2018-01-02 13:00:00+03:00  0.0000
2018-01-02 14:00:00+03:00 -0.0164
...                           ...
2023-09-22 14:00:00+03:00 -0.7000
2023-09-22 15:00:00+03:00  0.0000
2023-09-22 16:00:00+03:00  0.1000
2023-09-22 17:00:00+03:00  0.4000
2023-09-22 18:00:00+03:00  0.0000

[14226 rows x 1 columns]
ADF Statistic: -17.32985882953613
p-value: 5.418190371801927e-30
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood               -7661.180
Date:                Sun, 24 Dec 2023   AIC                          15386.360
Time:                        02:00:49   BIC                          15628.371
Sample:                             0   HQIC                         15466.865
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.7579      0.078      9.756      0.000       0.606       0.910
ar.L2         -0.0122      0.005     -2.425      0.015      -0.022      -0.002
ar.L3          0.0222      0.005      4.086      0.000       0.012       0.033
ar.L4          0.0053      0.006      0.947      0.344      -0.006       0.016
ar.L5         -0.0163      0.005     -3.023      0.002      -0.027      -0.006
ar.L6          0.0250      0.006      4.509      0.000       0.014       0.036
ar.L7          0.0238      0.006      4.238      0.000       0.013       0.035
ar.L8         -0.0495      0.006     -7.632      0.000      -0.062      -0.037
ar.L9          0.0474      0.006      8.192      0.000       0.036       0.059
ar.L10        -0.0197      0.005     -3.655      0.000      -0.030      -0.009
ar.L11        -0.0119      0.005     -2.514      0.012      -0.021      -0.003
ar.L12        -0.0060      0.006     -0.959      0.338      -0.018       0.006
ar.L13        -0.0040      0.006     -0.698      0.485      -0.015       0.007
ar.L14         0.0040      0.005      0.743      0.457      -0.007       0.015
ar.L15         0.0246      0.005      4.640      0.000       0.014       0.035
ar.L16        -0.0176      0.006     -3.067      0.002      -0.029      -0.006
ar.L17         0.0107      0.005      1.970      0.049    5.68e-05       0.021
ar.L18        -0.0150      0.006     -2.540      0.011      -0.027      -0.003
ar.L19        -0.0078      0.006     -1.286      0.198      -0.020       0.004
ar.L20         0.0345      0.006      6.246      0.000       0.024       0.045
ar.L21        -0.0098      0.006     -1.705      0.088      -0.021       0.001
ar.L22        -0.0183      0.006     -2.963      0.003      -0.030      -0.006
ar.L23        -0.0250      0.006     -4.159      0.000      -0.037      -0.013
ar.L24         0.0099      0.006      1.526      0.127      -0.003       0.023
ar.L25         0.0207      0.006      3.395      0.001       0.009       0.033
ar.L26         0.0009      0.006      0.152      0.879      -0.010       0.012
ar.L27        -0.0037      0.006     -0.602      0.547      -0.016       0.008
ar.L28        -0.0013      0.006     -0.214      0.830      -0.013       0.011
ar.L29         0.0094      0.006      1.590      0.112      -0.002       0.021
ar.L30         0.0086      0.005      1.598      0.110      -0.002       0.019
ma.L1         -0.7622      0.077     -9.851      0.000      -0.914      -0.611
sigma2         0.1719      0.001    292.717      0.000       0.171       0.173
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):            380223.27
Prob(Q):                              0.97   Prob(JB):                         0.00
Heteroskedasticity (H):              12.48   Skew:                             0.39
Prob(H) (two-sided):                  0.00   Kurtosis:                        28.32
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    119.326008
14228    119.314754
14229    119.465235
14230    119.641441
14231    119.792918
14232    119.930781
14233    119.961276
14234    119.913602
14235    119.865056
14236    119.972809
Name: predicted_mean, dtype: float64
       lower price  upper price
14227   118.513395   120.138621
14228   118.168017   120.461491
14229   118.068985   120.861486
14230   118.029705   121.253177
14231   117.986415   121.599420
14232   117.950798   121.910764
14233   117.815633   122.106919
14234   117.602454   122.224751
14235   117.405249   122.324864
14236   117.364387   122.581231

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 2.182208058147649
In [575]:
arima_forecast('TKFEN', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  49.16
2023-09-20 10:00:00+03:00  49.82
2023-09-20 11:00:00+03:00  49.36
2023-09-20 12:00:00+03:00  49.24
2023-09-20 13:00:00+03:00  48.66
2023-09-20 14:00:00+03:00  48.38
2023-09-20 15:00:00+03:00  48.12
2023-09-20 16:00:00+03:00  47.78
2023-09-20 17:00:00+03:00  47.78
2023-09-20 18:00:00+03:00  47.88
2023-09-21 09:00:00+03:00  47.58
2023-09-21 10:00:00+03:00  47.38
2023-09-21 11:00:00+03:00  47.92
2023-09-21 12:00:00+03:00  47.58
2023-09-21 13:00:00+03:00  47.88
2023-09-21 14:00:00+03:00  48.92
2023-09-21 15:00:00+03:00  49.08
2023-09-21 16:00:00+03:00  50.45
2023-09-21 17:00:00+03:00  50.95
2023-09-21 18:00:00+03:00  50.95
2023-09-22 09:00:00+03:00  53.80
2023-09-22 10:00:00+03:00  52.55
2023-09-22 11:00:00+03:00  52.35
2023-09-22 12:00:00+03:00  52.30
2023-09-22 13:00:00+03:00  52.55
2023-09-22 14:00:00+03:00  52.30
2023-09-22 15:00:00+03:00  52.20
2023-09-22 16:00:00+03:00  51.80
2023-09-22 17:00:00+03:00  52.00
2023-09-22 18:00:00+03:00  52.00
Mean of the first 10 values: price    52.09
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  52.20
2023-09-25 10:00:00+03:00  52.05
2023-09-25 11:00:00+03:00  51.90
2023-09-25 12:00:00+03:00  52.30
2023-09-25 13:00:00+03:00  52.25
2023-09-25 14:00:00+03:00  51.85
2023-09-25 15:00:00+03:00  52.00
2023-09-25 16:00:00+03:00  52.00
2023-09-25 17:00:00+03:00  52.05
2023-09-25 18:00:00+03:00  52.30
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.0600
2018-01-02 11:00:00+03:00 -0.0974
2018-01-02 12:00:00+03:00 -0.1123
2018-01-02 13:00:00+03:00  0.0000
2018-01-02 14:00:00+03:00  0.0075
...                           ...
2023-09-22 14:00:00+03:00 -0.2500
2023-09-22 15:00:00+03:00 -0.1000
2023-09-22 16:00:00+03:00 -0.4000
2023-09-22 17:00:00+03:00  0.2000
2023-09-22 18:00:00+03:00  0.0000

[14226 rows x 1 columns]
ADF Statistic: -17.0398452406441
p-value: 8.211241012274152e-30
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood                1919.611
Date:                Sun, 24 Dec 2023   AIC                          -3775.222
Time:                        02:01:21   BIC                          -3533.211
Sample:                             0   HQIC                         -3694.717
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.0091      0.642      0.014      0.989      -1.250       1.268
ar.L2          0.0051      0.013      0.395      0.693      -0.020       0.030
ar.L3          0.0089      0.006      1.573      0.116      -0.002       0.020
ar.L4          0.0171      0.007      2.459      0.014       0.003       0.031
ar.L5         -0.0110      0.012     -0.914      0.361      -0.035       0.013
ar.L6          0.0046      0.008      0.565      0.572      -0.011       0.021
ar.L7          0.0124      0.006      2.208      0.027       0.001       0.023
ar.L8         -0.0058      0.009     -0.634      0.526      -0.024       0.012
ar.L9          0.0092      0.006      1.625      0.104      -0.002       0.020
ar.L10         0.0060      0.007      0.826      0.409      -0.008       0.020
ar.L11        -0.0330      0.006     -5.345      0.000      -0.045      -0.021
ar.L12        -0.0196      0.022     -0.878      0.380      -0.063       0.024
ar.L13        -0.0105      0.013     -0.792      0.429      -0.036       0.015
ar.L14         0.0099      0.008      1.233      0.218      -0.006       0.026
ar.L15        -0.0073      0.008     -0.930      0.352      -0.023       0.008
ar.L16         0.0043      0.007      0.652      0.514      -0.009       0.017
ar.L17         0.0190      0.006      3.162      0.002       0.007       0.031
ar.L18        -0.0058      0.013     -0.436      0.663      -0.032       0.020
ar.L19         0.0209      0.007      3.180      0.001       0.008       0.034
ar.L20         0.0030      0.014      0.206      0.837      -0.025       0.031
ar.L21        -0.0222      0.005     -4.094      0.000      -0.033      -0.012
ar.L22         0.0096      0.016      0.609      0.542      -0.021       0.040
ar.L23         0.0257      0.008      3.344      0.001       0.011       0.041
ar.L24         0.0092      0.017      0.540      0.589      -0.024       0.043
ar.L25        -0.0113      0.008     -1.408      0.159      -0.027       0.004
ar.L26        -0.0109      0.009     -1.267      0.205      -0.028       0.006
ar.L27         0.0212      0.009      2.360      0.018       0.004       0.039
ar.L28        -0.0165      0.015     -1.119      0.263      -0.045       0.012
ar.L29         0.0124      0.012      1.041      0.298      -0.011       0.036
ar.L30        -0.0069      0.009     -0.760      0.447      -0.025       0.011
ma.L1          0.0091      0.642      0.014      0.989      -1.249       1.267
sigma2         0.0447      0.000    275.985      0.000       0.044       0.045
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):            338631.54
Prob(Q):                              0.99   Prob(JB):                         0.00
Heteroskedasticity (H):               6.21   Skew:                             0.24
Prob(H) (two-sided):                  0.00   Kurtosis:                        26.90
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    51.971148
14228    51.885568
14229    51.895459
14230    51.874521
14231    51.966910
14232    51.947435
14233    51.973830
14234    52.044477
14235    52.028773
14236    52.115400
Name: predicted_mean, dtype: float64
       lower price  upper price
14227    51.556759    52.385536
14228    51.294195    52.476940
14229    51.167740    52.623178
14230    51.030353    52.718689
14231    51.017248    52.916573
14232    50.904677    52.990193
14233    50.844906    53.102753
14234    50.833614    53.255340
14235    50.741921    53.315626
14236    50.755617    53.475182

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.19679655925987144
In [584]:
arima_forecast('TUPRS', 30, 1, 1)

                              price
timestamp                          
2023-09-20 09:00:00+03:00  138.3464
2023-09-20 10:00:00+03:00  137.3923
2023-09-20 11:00:00+03:00  136.2474
2023-09-20 12:00:00+03:00  135.9611
2023-09-20 13:00:00+03:00  135.9611
2023-09-20 14:00:00+03:00  136.0565
2023-09-20 15:00:00+03:00  135.5795
2023-09-20 16:00:00+03:00  134.7208
2023-09-20 17:00:00+03:00  133.9575
2023-09-20 18:00:00+03:00  134.8162
2023-09-21 09:00:00+03:00  134.0529
2023-09-21 10:00:00+03:00  133.0034
2023-09-21 11:00:00+03:00  133.2896
2023-09-21 12:00:00+03:00  133.3850
2023-09-21 13:00:00+03:00  134.5300
2023-09-21 14:00:00+03:00  137.5831
2023-09-21 15:00:00+03:00  138.1556
2023-09-21 16:00:00+03:00  139.5868
2023-09-21 17:00:00+03:00  141.0179
2023-09-21 18:00:00+03:00  141.0179
2023-09-22 09:00:00+03:00  141.2087
2023-09-22 10:00:00+03:00  145.7885
2023-09-22 11:00:00+03:00  146.3610
2023-09-22 12:00:00+03:00  147.4105
2023-09-22 13:00:00+03:00  146.6472
2023-09-22 14:00:00+03:00  145.8839
2023-09-22 15:00:00+03:00  147.0288
2023-09-22 16:00:00+03:00  147.8875
2023-09-22 17:00:00+03:00  148.0784
2023-09-22 18:00:00+03:00  147.0288
Mean of the first 10 values: price    155.96888
dtype: float64
                              price
timestamp                          
2023-09-25 09:00:00+03:00  150.0820
2023-09-25 10:00:00+03:00  153.3260
2023-09-25 11:00:00+03:00  154.8526
2023-09-25 12:00:00+03:00  157.2379
2023-09-25 13:00:00+03:00  156.2837
2023-09-25 14:00:00+03:00  155.9975
2023-09-25 15:00:00+03:00  157.8103
2023-09-25 16:00:00+03:00  156.8562
2023-09-25 17:00:00+03:00  158.8598
2023-09-25 18:00:00+03:00  158.3828
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.0703
2018-01-02 11:00:00+03:00 -0.0201
2018-01-02 12:00:00+03:00  0.0101
2018-01-02 13:00:00+03:00  0.0200
2018-01-02 14:00:00+03:00  0.0302
...                           ...
2023-09-22 14:00:00+03:00 -0.7633
2023-09-22 15:00:00+03:00  1.1449
2023-09-22 16:00:00+03:00  0.8587
2023-09-22 17:00:00+03:00  0.1909
2023-09-22 18:00:00+03:00 -1.0496

[14226 rows x 1 columns]
ADF Statistic: -16.94374339705925
p-value: 9.574477067538152e-30
Critical Values: {'1%': -3.4308111170220643, '5%': -2.8617437929148606, '10%': -2.566878474199101}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood               -4191.410
Date:                Sun, 24 Dec 2023   AIC                           8446.821
Time:                        13:26:50   BIC                           8688.831
Sample:                             0   HQIC                          8527.325
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.2832      0.143      1.976      0.048       0.002       0.564
ar.L2         -0.0058      0.008     -0.720      0.471      -0.022       0.010
ar.L3          0.0331      0.003     10.065      0.000       0.027       0.040
ar.L4         -0.0230      0.006     -3.831      0.000      -0.035      -0.011
ar.L5          0.0168      0.004      4.126      0.000       0.009       0.025
ar.L6          0.0612      0.004     15.615      0.000       0.053       0.069
ar.L7         -0.0020      0.010     -0.195      0.845      -0.022       0.018
ar.L8         -0.0002      0.004     -0.042      0.966      -0.009       0.008
ar.L9          0.0071      0.004      1.891      0.059      -0.000       0.015
ar.L10         0.0186      0.003      7.354      0.000       0.014       0.024
ar.L11        -0.0134      0.005     -2.868      0.004      -0.023      -0.004
ar.L12        -0.0549      0.004    -14.523      0.000      -0.062      -0.047
ar.L13        -0.0074      0.008     -0.877      0.380      -0.024       0.009
ar.L14        -0.0043      0.005     -0.908      0.364      -0.013       0.005
ar.L15        -0.0207      0.003     -6.386      0.000      -0.027      -0.014
ar.L16        -0.0222      0.005     -4.476      0.000      -0.032      -0.012
ar.L17         0.0418      0.005      8.354      0.000       0.032       0.052
ar.L18        -0.0381      0.007     -5.589      0.000      -0.051      -0.025
ar.L19         0.0173      0.005      3.174      0.002       0.007       0.028
ar.L20        -0.0070      0.003     -2.126      0.034      -0.014      -0.001
ar.L21        -0.0054      0.004     -1.482      0.138      -0.013       0.002
ar.L22         0.0446      0.004     11.482      0.000       0.037       0.052
ar.L23         0.0165      0.007      2.236      0.025       0.002       0.031
ar.L24        -0.0154      0.005     -2.830      0.005      -0.026      -0.005
ar.L25         0.0137      0.004      3.286      0.001       0.006       0.022
ar.L26         0.0187      0.004      4.617      0.000       0.011       0.027
ar.L27        -0.0213      0.005     -4.577      0.000      -0.030      -0.012
ar.L28        -0.0129      0.005     -2.550      0.011      -0.023      -0.003
ar.L29         0.0501      0.005     10.940      0.000       0.041       0.059
ar.L30        -0.0358      0.006     -5.695      0.000      -0.048      -0.023
ma.L1         -0.2322      0.143     -1.622      0.105      -0.513       0.048
sigma2         0.1055      0.000    366.833      0.000       0.105       0.106
===================================================================================
Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):           2237842.44
Prob(Q):                              0.93   Prob(JB):                         0.00
Heteroskedasticity (H):              34.23   Skew:                             2.34
Prob(H) (two-sided):                  0.00   Kurtosis:                        64.26
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    146.670404
14228    146.601191
14229    146.613837
14230    146.221092
14231    146.092269
14232    145.926621
14233    145.751107
14234    145.790524
14235    146.024577
14236    145.805277
Name: predicted_mean, dtype: float64
       lower price  upper price
14227   146.033660   147.307147
14228   145.677424   147.524958
14229   145.469883   147.757790
14230   144.881425   147.560758
14231   144.585375   147.599162
14232   144.265959   147.587283
14233   143.932903   147.569312
14234   143.822810   147.758239
14235   143.916461   148.132692
14236   143.562793   148.047760

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 10.208856797991869
In [577]:
arima_forecast('TTKOM', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  23.72
2023-09-20 10:00:00+03:00  23.86
2023-09-20 11:00:00+03:00  23.64
2023-09-20 12:00:00+03:00  23.40
2023-09-20 13:00:00+03:00  23.40
2023-09-20 14:00:00+03:00  23.48
2023-09-20 15:00:00+03:00  23.58
2023-09-20 16:00:00+03:00  23.24
2023-09-20 17:00:00+03:00  22.68
2023-09-20 18:00:00+03:00  22.64
2023-09-21 09:00:00+03:00  22.68
2023-09-21 10:00:00+03:00  22.56
2023-09-21 11:00:00+03:00  22.60
2023-09-21 12:00:00+03:00  22.42
2023-09-21 13:00:00+03:00  22.52
2023-09-21 14:00:00+03:00  23.12
2023-09-21 15:00:00+03:00  22.94
2023-09-21 16:00:00+03:00  22.98
2023-09-21 17:00:00+03:00  23.26
2023-09-21 18:00:00+03:00  23.34
2023-09-22 09:00:00+03:00  23.38
2023-09-22 10:00:00+03:00  23.34
2023-09-22 11:00:00+03:00  23.62
2023-09-22 12:00:00+03:00  23.48
2023-09-22 13:00:00+03:00  23.42
2023-09-22 14:00:00+03:00  23.22
2023-09-22 15:00:00+03:00  23.18
2023-09-22 16:00:00+03:00  23.22
2023-09-22 17:00:00+03:00  23.20
2023-09-22 18:00:00+03:00  23.18
Mean of the first 10 values: price    23.638
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  23.40
2023-09-25 10:00:00+03:00  23.62
2023-09-25 11:00:00+03:00  23.52
2023-09-25 12:00:00+03:00  23.70
2023-09-25 13:00:00+03:00  23.70
2023-09-25 14:00:00+03:00  23.52
2023-09-25 15:00:00+03:00  23.48
2023-09-25 16:00:00+03:00  23.56
2023-09-25 17:00:00+03:00  23.94
2023-09-25 18:00:00+03:00  23.94
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.1123
2018-01-02 11:00:00+03:00  0.0000
2018-01-02 12:00:00+03:00 -0.0161
2018-01-02 13:00:00+03:00  0.0000
2018-01-02 14:00:00+03:00  0.0241
...                           ...
2023-09-22 14:00:00+03:00 -0.2000
2023-09-22 15:00:00+03:00 -0.0400
2023-09-22 16:00:00+03:00  0.0400
2023-09-22 17:00:00+03:00 -0.0200
2023-09-22 18:00:00+03:00 -0.0200

[14226 rows x 1 columns]
ADF Statistic: -16.887173836627433
p-value: 1.0518963206021561e-29
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood               11745.489
Date:                Sun, 24 Dec 2023   AIC                         -23426.978
Time:                        02:03:00   BIC                         -23184.967
Sample:                             0   HQIC                        -23346.473
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.0168      0.556     -0.030      0.976      -1.107       1.073
ar.L2         -0.0381      0.020     -1.942      0.052      -0.077       0.000
ar.L3          0.0108      0.022      0.488      0.626      -0.032       0.054
ar.L4          0.0033      0.007      0.485      0.628      -0.010       0.017
ar.L5          0.0066      0.004      1.885      0.059      -0.000       0.013
ar.L6          0.0398      0.005      7.730      0.000       0.030       0.050
ar.L7         -0.0098      0.023     -0.436      0.663      -0.054       0.034
ar.L8         -0.0126      0.006     -2.200      0.028      -0.024      -0.001
ar.L9          0.0182      0.008      2.307      0.021       0.003       0.034
ar.L10         0.0377      0.010      3.801      0.000       0.018       0.057
ar.L11        -0.0380      0.021     -1.795      0.073      -0.079       0.003
ar.L12        -0.0226      0.021     -1.087      0.277      -0.063       0.018
ar.L13         0.0267      0.014      1.973      0.048       0.000       0.053
ar.L14         0.0046      0.015      0.311      0.756      -0.025       0.034
ar.L15         0.0200      0.005      4.036      0.000       0.010       0.030
ar.L16         0.0018      0.012      0.147      0.883      -0.022       0.026
ar.L17         0.0368      0.004      9.542      0.000       0.029       0.044
ar.L18        -0.0074      0.020     -0.363      0.717      -0.047       0.033
ar.L19         0.0509      0.005     10.273      0.000       0.041       0.061
ar.L20         0.0077      0.028      0.273      0.785      -0.047       0.063
ar.L21        -0.0055      0.007     -0.840      0.401      -0.018       0.007
ar.L22        -0.0168      0.005     -3.738      0.000      -0.026      -0.008
ar.L23         0.0084      0.011      0.800      0.423      -0.012       0.029
ar.L24        -0.0072      0.006     -1.260      0.208      -0.018       0.004
ar.L25        -0.0277      0.005     -5.048      0.000      -0.038      -0.017
ar.L26        -0.0294      0.016     -1.852      0.064      -0.061       0.002
ar.L27         0.0221      0.017      1.321      0.186      -0.011       0.055
ar.L28        -0.0089      0.012     -0.725      0.469      -0.033       0.015
ar.L29         0.0083      0.006      1.329      0.184      -0.004       0.020
ar.L30         0.0061      0.006      1.109      0.267      -0.005       0.017
ma.L1         -0.0177      0.556     -0.032      0.975      -1.108       1.072
sigma2         0.0112   3.18e-05    353.129      0.000       0.011       0.011
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           1538455.41
Prob(Q):                              1.00   Prob(JB):                         0.00
Heteroskedasticity (H):              28.54   Skew:                             1.00
Prob(H) (two-sided):                  0.00   Kurtosis:                        53.91
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    23.168744
14228    23.169485
14229    23.204598
14230    23.219760
14231    23.254599
14232    23.259209
14233    23.261880
14234    23.278556
14235    23.278964
14236    23.284067
Name: predicted_mean, dtype: float64
       lower price  upper price
14227    22.961049    23.376438
14228    22.880788    23.458183
14229    22.857479    23.551717
14230    22.821439    23.618082
14231    22.810566    23.698633
14232    22.773284    23.745135
14233    22.734267    23.789493
14234    22.713234    23.843877
14235    22.679334    23.878595
14236    22.650612    23.917523

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.42976132619291785
In [578]:
arima_forecast('TCELL', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  54.30
2023-09-20 10:00:00+03:00  54.75
2023-09-20 11:00:00+03:00  54.25
2023-09-20 12:00:00+03:00  54.25
2023-09-20 13:00:00+03:00  54.40
2023-09-20 14:00:00+03:00  54.65
2023-09-20 15:00:00+03:00  54.35
2023-09-20 16:00:00+03:00  54.15
2023-09-20 17:00:00+03:00  53.75
2023-09-20 18:00:00+03:00  53.40
2023-09-21 09:00:00+03:00  53.10
2023-09-21 10:00:00+03:00  53.10
2023-09-21 11:00:00+03:00  52.95
2023-09-21 12:00:00+03:00  52.90
2023-09-21 13:00:00+03:00  53.15
2023-09-21 14:00:00+03:00  54.75
2023-09-21 15:00:00+03:00  54.35
2023-09-21 16:00:00+03:00  54.40
2023-09-21 17:00:00+03:00  55.40
2023-09-21 18:00:00+03:00  55.40
2023-09-22 09:00:00+03:00  55.40
2023-09-22 10:00:00+03:00  54.85
2023-09-22 11:00:00+03:00  54.75
2023-09-22 12:00:00+03:00  54.75
2023-09-22 13:00:00+03:00  54.60
2023-09-22 14:00:00+03:00  54.40
2023-09-22 15:00:00+03:00  54.50
2023-09-22 16:00:00+03:00  54.55
2023-09-22 17:00:00+03:00  54.10
2023-09-22 18:00:00+03:00  54.45
Mean of the first 10 values: price    54.815
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  54.60
2023-09-25 10:00:00+03:00  54.60
2023-09-25 11:00:00+03:00  54.45
2023-09-25 12:00:00+03:00  54.85
2023-09-25 13:00:00+03:00  54.95
2023-09-25 14:00:00+03:00  54.85
2023-09-25 15:00:00+03:00  54.65
2023-09-25 16:00:00+03:00  55.00
2023-09-25 17:00:00+03:00  55.00
2023-09-25 18:00:00+03:00  55.20
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.0392
2018-01-02 11:00:00+03:00 -0.1018
2018-01-02 12:00:00+03:00 -0.0077
2018-01-02 13:00:00+03:00  0.0548
2018-01-02 14:00:00+03:00  0.0547
...                           ...
2023-09-22 14:00:00+03:00 -0.2000
2023-09-22 15:00:00+03:00  0.1000
2023-09-22 16:00:00+03:00  0.0500
2023-09-22 17:00:00+03:00 -0.4500
2023-09-22 18:00:00+03:00  0.3500

[14226 rows x 1 columns]
ADF Statistic: -18.557373182184367
p-value: 2.0892203476712126e-30
Critical Values: {'1%': -3.4308111170220643, '5%': -2.8617437929148606, '10%': -2.566878474199101}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood                2889.472
Date:                Sun, 24 Dec 2023   AIC                          -5714.944
Time:                        02:04:15   BIC                          -5472.933
Sample:                             0   HQIC                         -5634.439
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.4563      0.093     -4.912      0.000      -0.638      -0.274
ar.L2         -0.0304      0.005     -6.482      0.000      -0.040      -0.021
ar.L3         -0.0436      0.004     -9.768      0.000      -0.052      -0.035
ar.L4         -0.0231      0.006     -4.183      0.000      -0.034      -0.012
ar.L5         -0.0108      0.005     -2.338      0.019      -0.020      -0.002
ar.L6          0.0363      0.005      7.634      0.000       0.027       0.046
ar.L7          0.0185      0.006      3.301      0.001       0.008       0.029
ar.L8         -0.0287      0.004     -6.987      0.000      -0.037      -0.021
ar.L9          0.0397      0.005      8.628      0.000       0.031       0.049
ar.L10         0.0090      0.006      1.426      0.154      -0.003       0.021
ar.L11        -0.0100      0.004     -2.668      0.008      -0.017      -0.003
ar.L12        -0.0142      0.004     -3.750      0.000      -0.022      -0.007
ar.L13        -0.0032      0.004     -0.789      0.430      -0.011       0.005
ar.L14         0.0177      0.004      4.274      0.000       0.010       0.026
ar.L15        -0.0090      0.005     -1.821      0.069      -0.019       0.001
ar.L16        -0.0073      0.005     -1.514      0.130      -0.017       0.002
ar.L17         0.0007      0.005      0.155      0.877      -0.009       0.010
ar.L18        -0.0045      0.005     -0.992      0.321      -0.013       0.004
ar.L19        -0.0002      0.004     -0.056      0.955      -0.009       0.008
ar.L20         0.0271      0.004      7.328      0.000       0.020       0.034
ar.L21        -0.0034      0.004     -0.766      0.443      -0.012       0.005
ar.L22         0.0025      0.004      0.567      0.571      -0.006       0.011
ar.L23        -0.0281      0.004     -6.933      0.000      -0.036      -0.020
ar.L24        -0.0163      0.005     -2.986      0.003      -0.027      -0.006
ar.L25        -0.0199      0.004     -4.549      0.000      -0.028      -0.011
ar.L26        -0.0229      0.005     -5.070      0.000      -0.032      -0.014
ar.L27        -0.0048      0.004     -1.145      0.252      -0.013       0.003
ar.L28        -0.0053      0.005     -1.087      0.277      -0.015       0.004
ar.L29        -0.0068      0.004     -1.514      0.130      -0.016       0.002
ar.L30        -0.0329      0.004     -8.578      0.000      -0.040      -0.025
ma.L1          0.4257      0.093      4.579      0.000       0.244       0.608
sigma2         0.0390      0.000    308.754      0.000       0.039       0.039
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):            561445.59
Prob(Q):                              1.00   Prob(JB):                         0.00
Heteroskedasticity (H):              21.79   Skew:                             0.73
Prob(H) (two-sided):                  0.00   Kurtosis:                        33.74
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    54.361099
14228    54.382224
14229    54.435742
14230    54.431588
14231    54.422511
14232    54.480162
14233    54.484683
14234    54.475618
14235    54.486972
14236    54.476507
Name: predicted_mean, dtype: float64
       lower price  upper price
14227    53.974030    54.748168
14228    53.843136    54.921312
14229    53.782542    55.088942
14230    53.688029    55.175146
14231    53.599318    55.245704
14232    53.585283    55.375042
14233    53.517478    55.451887
14234    53.441151    55.510085
14235    53.393220    55.580723
14236    53.320252    55.632761

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.42234389490438423
In [579]:
arima_forecast('HALKB', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  15.39
2023-09-20 10:00:00+03:00  15.59
2023-09-20 11:00:00+03:00  15.67
2023-09-20 12:00:00+03:00  15.68
2023-09-20 13:00:00+03:00  15.67
2023-09-20 14:00:00+03:00  15.52
2023-09-20 15:00:00+03:00  15.57
2023-09-20 16:00:00+03:00  15.55
2023-09-20 17:00:00+03:00  15.36
2023-09-20 18:00:00+03:00  15.38
2023-09-21 09:00:00+03:00  15.38
2023-09-21 10:00:00+03:00  15.61
2023-09-21 11:00:00+03:00  15.50
2023-09-21 12:00:00+03:00  15.51
2023-09-21 13:00:00+03:00  15.66
2023-09-21 14:00:00+03:00  15.34
2023-09-21 15:00:00+03:00  15.34
2023-09-21 16:00:00+03:00  15.41
2023-09-21 17:00:00+03:00  15.51
2023-09-21 18:00:00+03:00  15.50
2023-09-22 09:00:00+03:00  15.59
2023-09-22 10:00:00+03:00  15.45
2023-09-22 11:00:00+03:00  15.47
2023-09-22 12:00:00+03:00  15.44
2023-09-22 13:00:00+03:00  15.50
2023-09-22 14:00:00+03:00  15.36
2023-09-22 15:00:00+03:00  15.32
2023-09-22 16:00:00+03:00  15.32
2023-09-22 17:00:00+03:00  15.20
2023-09-22 18:00:00+03:00  15.23
Mean of the first 10 values: price    15.467
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  15.32
2023-09-25 10:00:00+03:00  15.44
2023-09-25 11:00:00+03:00  15.45
2023-09-25 12:00:00+03:00  15.47
2023-09-25 13:00:00+03:00  15.67
2023-09-25 14:00:00+03:00  15.47
2023-09-25 15:00:00+03:00  15.41
2023-09-25 16:00:00+03:00  15.50
2023-09-25 17:00:00+03:00  15.49
2023-09-25 18:00:00+03:00  15.45
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.3219
2018-01-02 11:00:00+03:00 -0.0195
2018-01-02 12:00:00+03:00 -0.0976
2018-01-02 13:00:00+03:00  0.0195
2018-01-02 14:00:00+03:00  0.0586
...                           ...
2023-09-22 14:00:00+03:00 -0.1400
2023-09-22 15:00:00+03:00 -0.0400
2023-09-22 16:00:00+03:00  0.0000
2023-09-22 17:00:00+03:00 -0.1200
2023-09-22 18:00:00+03:00  0.0300

[14226 rows x 1 columns]
ADF Statistic: -16.312651299834577
p-value: 3.1847512340477354e-29
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood               15534.967
Date:                Sun, 24 Dec 2023   AIC                         -31005.933
Time:                        02:05:16   BIC                         -30763.923
Sample:                             0   HQIC                        -30925.428
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.0231      0.129      0.178      0.859      -0.231       0.277
ar.L2          0.0066      0.008      0.836      0.403      -0.009       0.022
ar.L3          0.0365      0.004      8.246      0.000       0.028       0.045
ar.L4         -0.0015      0.007     -0.226      0.821      -0.015       0.012
ar.L5         -0.0133      0.005     -2.521      0.012      -0.024      -0.003
ar.L6          0.0155      0.005      3.136      0.002       0.006       0.025
ar.L7         -0.0115      0.005     -2.158      0.031      -0.022      -0.001
ar.L8         -0.0013      0.005     -0.257      0.797      -0.011       0.008
ar.L9          0.0173      0.004      4.001      0.000       0.009       0.026
ar.L10         0.0327      0.005      7.169      0.000       0.024       0.042
ar.L11         0.0068      0.006      1.177      0.239      -0.005       0.018
ar.L12         0.0038      0.004      0.868      0.385      -0.005       0.012
ar.L13        -0.0107      0.005     -2.253      0.024      -0.020      -0.001
ar.L14         0.0257      0.006      4.585      0.000       0.015       0.037
ar.L15        -0.0003      0.006     -0.050      0.960      -0.013       0.012
ar.L16         0.0048      0.006      0.876      0.381      -0.006       0.016
ar.L17         0.0011      0.005      0.246      0.806      -0.008       0.010
ar.L18        -0.0307      0.005     -6.342      0.000      -0.040      -0.021
ar.L19         0.0137      0.005      2.675      0.007       0.004       0.024
ar.L20         0.0389      0.004      9.322      0.000       0.031       0.047
ar.L21         0.0441      0.007      6.136      0.000       0.030       0.058
ar.L22        -0.0138      0.007     -1.965      0.049      -0.028   -3.27e-05
ar.L23        -0.0182      0.005     -3.407      0.001      -0.029      -0.008
ar.L24         0.0131      0.007      1.987      0.047       0.000       0.026
ar.L25        -0.0197      0.006     -3.265      0.001      -0.032      -0.008
ar.L26         0.0297      0.006      5.188      0.000       0.018       0.041
ar.L27         0.0147      0.006      2.447      0.014       0.003       0.026
ar.L28        -0.0009      0.006     -0.149      0.881      -0.012       0.011
ar.L29        -0.0109      0.005     -2.066      0.039      -0.021      -0.001
ar.L30         0.0324      0.004      8.514      0.000       0.025       0.040
ma.L1          0.0228      0.130      0.176      0.861      -0.232       0.277
sigma2         0.0066   1.96e-05    336.839      0.000       0.007       0.007
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           2000231.22
Prob(Q):                              0.99   Prob(JB):                         0.00
Heteroskedasticity (H):               4.25   Skew:                             0.52
Prob(H) (two-sided):                  0.00   Kurtosis:                        61.08
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    15.248607
14228    15.248312
14229    15.253162
14230    15.263828
14231    15.259825
14232    15.242137
14233    15.219299
14234    15.229929
14235    15.234112
14236    15.231369
Name: predicted_mean, dtype: float64
       lower price  upper price
14227    15.089483    15.407731
14228    15.018058    15.478567
14229    14.968349    15.537975
14230    14.930324    15.597331
14231    14.883807    15.635843
14232    14.828798    15.655475
14233    14.770765    15.667833
14234    14.749379    15.710478
14235    14.723701    15.744524
14236    14.691818    15.770920

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.2378744053801174
In [580]:
arima_forecast('ISCTR', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  22.20
2023-09-20 10:00:00+03:00  22.26
2023-09-20 11:00:00+03:00  22.80
2023-09-20 12:00:00+03:00  22.72
2023-09-20 13:00:00+03:00  22.74
2023-09-20 14:00:00+03:00  22.74
2023-09-20 15:00:00+03:00  22.86
2023-09-20 16:00:00+03:00  22.80
2023-09-20 17:00:00+03:00  22.50
2023-09-20 18:00:00+03:00  22.50
2023-09-21 09:00:00+03:00  22.54
2023-09-21 10:00:00+03:00  22.74
2023-09-21 11:00:00+03:00  22.70
2023-09-21 12:00:00+03:00  22.76
2023-09-21 13:00:00+03:00  22.96
2023-09-21 14:00:00+03:00  22.66
2023-09-21 15:00:00+03:00  22.72
2023-09-21 16:00:00+03:00  22.98
2023-09-21 17:00:00+03:00  23.00
2023-09-21 18:00:00+03:00  23.14
2023-09-22 09:00:00+03:00  23.18
2023-09-22 10:00:00+03:00  23.16
2023-09-22 11:00:00+03:00  23.58
2023-09-22 12:00:00+03:00  23.48
2023-09-22 13:00:00+03:00  24.40
2023-09-22 14:00:00+03:00  24.24
2023-09-22 15:00:00+03:00  24.08
2023-09-22 16:00:00+03:00  24.78
2023-09-22 17:00:00+03:00  24.96
2023-09-22 18:00:00+03:00  24.90
Mean of the first 10 values: price    25.218
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  25.04
2023-09-25 10:00:00+03:00  25.04
2023-09-25 11:00:00+03:00  25.28
2023-09-25 12:00:00+03:00  25.20
2023-09-25 13:00:00+03:00  25.24
2023-09-25 14:00:00+03:00  25.40
2023-09-25 15:00:00+03:00  25.32
2023-09-25 16:00:00+03:00  25.28
2023-09-25 17:00:00+03:00  25.22
2023-09-25 18:00:00+03:00  25.16
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.0113
2018-01-02 11:00:00+03:00  0.0000
2018-01-02 12:00:00+03:00 -0.0075
2018-01-02 13:00:00+03:00  0.0000
2018-01-02 14:00:00+03:00  0.0300
...                           ...
2023-09-22 14:00:00+03:00 -0.1600
2023-09-22 15:00:00+03:00 -0.1600
2023-09-22 16:00:00+03:00  0.7000
2023-09-22 17:00:00+03:00  0.1800
2023-09-22 18:00:00+03:00 -0.0600

[14226 rows x 1 columns]
ADF Statistic: -15.356857191577735
p-value: 3.6568880200845955e-28
Critical Values: {'1%': -3.4308111170220643, '5%': -2.8617437929148606, '10%': -2.566878474199101}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood               17865.450
Date:                Sun, 24 Dec 2023   AIC                         -35666.900
Time:                        02:05:54   BIC                         -35424.889
Sample:                             0   HQIC                        -35586.395
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.0170     13.553      0.001      0.999     -26.546      26.580
ar.L2         -0.0337      0.491     -0.069      0.945      -0.997       0.929
ar.L3          0.0336      0.467      0.072      0.943      -0.882       0.949
ar.L4         -0.0081      0.464     -0.017      0.986      -0.917       0.901
ar.L5          0.0352      0.118      0.297      0.766      -0.197       0.267
ar.L6          0.0220      0.479      0.046      0.963      -0.917       0.961
ar.L7          0.0078      0.289      0.027      0.978      -0.558       0.574
ar.L8          0.0234      0.101      0.232      0.817      -0.174       0.221
ar.L9         -0.0027      0.315     -0.009      0.993      -0.621       0.615
ar.L10         0.0441      0.043      1.039      0.299      -0.039       0.127
ar.L11         0.0301      0.599      0.050      0.960      -1.144       1.204
ar.L12        -0.0145      0.396     -0.037      0.971      -0.791       0.762
ar.L13         0.0181      0.204      0.089      0.929      -0.381       0.417
ar.L14         0.0045      0.250      0.018      0.986      -0.485       0.494
ar.L15         0.0056      0.056      0.100      0.920      -0.105       0.116
ar.L16        -0.0070      0.075     -0.093      0.926      -0.155       0.140
ar.L17         0.0462      0.097      0.478      0.632      -0.143       0.236
ar.L18         0.0203      0.629      0.032      0.974      -1.212       1.253
ar.L19         0.0025      0.264      0.010      0.992      -0.515       0.520
ar.L20         0.0235      0.029      0.810      0.418      -0.033       0.080
ar.L21        -0.0104      0.317     -0.033      0.974      -0.633       0.612
ar.L22         0.0055      0.148      0.037      0.970      -0.284       0.295
ar.L23         0.0006      0.078      0.008      0.994      -0.152       0.154
ar.L24         0.0098      0.008      1.234      0.217      -0.006       0.025
ar.L25        -0.0050      0.132     -0.038      0.970      -0.264       0.254
ar.L26         0.0252      0.071      0.355      0.723      -0.114       0.165
ar.L27         0.0190      0.343      0.055      0.956      -0.654       0.692
ar.L28        -0.0224      0.251     -0.089      0.929      -0.515       0.470
ar.L29        -0.0217      0.308     -0.071      0.944      -0.625       0.582
ar.L30        -0.0002      0.289     -0.001      1.000      -0.566       0.565
ma.L1          0.0193     13.553      0.001      0.999     -26.544      26.582
sigma2         0.0047   1.26e-05    376.543      0.000       0.005       0.005
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           2170716.18
Prob(Q):                              0.98   Prob(JB):                         0.00
Heteroskedasticity (H):              51.71   Skew:                             1.07
Prob(H) (two-sided):                  0.00   Kurtosis:                        63.48
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    24.935824
14228    24.939414
14229    24.986645
14230    25.013415
14231    25.053582
14232    25.094191
14233    25.092154
14234    25.160995
14235    25.197084
14236    25.207361
Name: predicted_mean, dtype: float64
       lower price  upper price
14227    24.800746    25.070902
14228    24.744889    25.133939
14229    24.749578    25.223712
14230    24.738199    25.288631
14231    24.745213    25.361950
14232    24.754067    25.434314
14233    24.721654    25.462655
14234    24.762108    25.559882
14235    24.770541    25.623627
14236    24.754885    25.659837

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.18410772858785132
In [581]:
arima_forecast('VAKBN', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  13.60
2023-09-20 10:00:00+03:00  13.81
2023-09-20 11:00:00+03:00  13.91
2023-09-20 12:00:00+03:00  13.86
2023-09-20 13:00:00+03:00  13.89
2023-09-20 14:00:00+03:00  13.83
2023-09-20 15:00:00+03:00  13.85
2023-09-20 16:00:00+03:00  13.77
2023-09-20 17:00:00+03:00  13.52
2023-09-20 18:00:00+03:00  13.46
2023-09-21 09:00:00+03:00  13.46
2023-09-21 10:00:00+03:00  13.62
2023-09-21 11:00:00+03:00  13.54
2023-09-21 12:00:00+03:00  13.58
2023-09-21 13:00:00+03:00  13.69
2023-09-21 14:00:00+03:00  13.52
2023-09-21 15:00:00+03:00  13.49
2023-09-21 16:00:00+03:00  13.59
2023-09-21 17:00:00+03:00  13.63
2023-09-21 18:00:00+03:00  13.64
2023-09-22 09:00:00+03:00  13.65
2023-09-22 10:00:00+03:00  13.69
2023-09-22 11:00:00+03:00  13.75
2023-09-22 12:00:00+03:00  13.70
2023-09-22 13:00:00+03:00  13.79
2023-09-22 14:00:00+03:00  13.58
2023-09-22 15:00:00+03:00  13.53
2023-09-22 16:00:00+03:00  13.56
2023-09-22 17:00:00+03:00  13.49
2023-09-22 18:00:00+03:00  13.50
Mean of the first 10 values: price    13.799
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  13.59
2023-09-25 10:00:00+03:00  13.64
2023-09-25 11:00:00+03:00  13.72
2023-09-25 12:00:00+03:00  13.85
2023-09-25 13:00:00+03:00  13.93
2023-09-25 14:00:00+03:00  13.85
2023-09-25 15:00:00+03:00  13.80
2023-09-25 16:00:00+03:00  13.86
2023-09-25 17:00:00+03:00  13.85
2023-09-25 18:00:00+03:00  13.90
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.1085
2018-01-02 11:00:00+03:00  0.0788
2018-01-02 12:00:00+03:00  0.0000
2018-01-02 13:00:00+03:00  0.0099
2018-01-02 14:00:00+03:00  0.0394
...                           ...
2023-09-22 14:00:00+03:00 -0.2100
2023-09-22 15:00:00+03:00 -0.0500
2023-09-22 16:00:00+03:00  0.0300
2023-09-22 17:00:00+03:00 -0.0700
2023-09-22 18:00:00+03:00  0.0100

[14226 rows x 1 columns]
ADF Statistic: -15.690217287205973
p-value: 1.4368998336235054e-28
Critical Values: {'1%': -3.430811149539904, '5%': -2.8617438072851624, '10%': -2.5668784818482697}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood               18049.059
Date:                Sun, 24 Dec 2023   AIC                         -36034.118
Time:                        02:06:49   BIC                         -35792.108
Sample:                             0   HQIC                        -35953.614
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1       -7.69e-05      0.187     -0.000      1.000      -0.367       0.367
ar.L2          0.0119      0.004      2.755      0.006       0.003       0.020
ar.L3          0.0481      0.005     10.343      0.000       0.039       0.057
ar.L4         -0.0224      0.010     -2.282      0.022      -0.042      -0.003
ar.L5         -0.0030      0.006     -0.488      0.626      -0.015       0.009
ar.L6          0.0116      0.004      2.613      0.009       0.003       0.020
ar.L7         -0.0043      0.005     -0.875      0.382      -0.014       0.005
ar.L8          0.0162      0.004      4.056      0.000       0.008       0.024
ar.L9          0.0092      0.005      1.847      0.065      -0.001       0.019
ar.L10         0.0570      0.003     17.729      0.000       0.051       0.063
ar.L11         0.0097      0.012      0.793      0.428      -0.014       0.034
ar.L12        -0.0157      0.006     -2.790      0.005      -0.027      -0.005
ar.L13        -0.0108      0.006     -1.828      0.068      -0.022       0.001
ar.L14         0.0286      0.006      4.950      0.000       0.017       0.040
ar.L15        -0.0020      0.007     -0.280      0.779      -0.016       0.012
ar.L16         0.0031      0.005      0.684      0.494      -0.006       0.012
ar.L17         0.0038      0.005      0.767      0.443      -0.006       0.014
ar.L18        -0.0167      0.004     -3.909      0.000      -0.025      -0.008
ar.L19         0.0409      0.005      7.693      0.000       0.030       0.051
ar.L20         0.0725      0.009      8.434      0.000       0.056       0.089
ar.L21         0.0067      0.016      0.424      0.672      -0.024       0.038
ar.L22        -0.0117      0.004     -2.859      0.004      -0.020      -0.004
ar.L23        -0.0193      0.005     -4.009      0.000      -0.029      -0.010
ar.L24         0.0091      0.007      1.290      0.197      -0.005       0.023
ar.L25        -0.0098      0.005     -1.819      0.069      -0.020       0.001
ar.L26         0.0227      0.005      4.136      0.000       0.012       0.034
ar.L27         0.0110      0.007      1.683      0.092      -0.002       0.024
ar.L28         0.0113      0.004      2.626      0.009       0.003       0.020
ar.L29         0.0364      0.005      7.095      0.000       0.026       0.046
ar.L30         0.0254      0.007      3.434      0.001       0.011       0.040
ma.L1         -0.0007      0.187     -0.004      0.997      -0.368       0.367
sigma2         0.0046   1.27e-05    364.648      0.000       0.005       0.005
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           2785477.15
Prob(Q):                              0.97   Prob(JB):                         0.00
Heteroskedasticity (H):               8.23   Skew:                             0.11
Prob(H) (two-sided):                  0.00   Kurtosis:                        71.55
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    13.527284
14228    13.544700
14229    13.547655
14230    13.551834
14231    13.544900
14232    13.517635
14233    13.502864
14234    13.505235
14235    13.506193
14236    13.498380
Name: predicted_mean, dtype: float64
       lower price  upper price
14227    13.393939    13.660630
14228    13.356193    13.733206
14229    13.315891    13.779419
14230    13.280424    13.823243
14231    13.240301    13.849500
14232    13.183223    13.852048
14233    13.140404    13.865323
14234    13.117059    13.893410
14235    13.093144    13.919242
14236    13.061421    13.935338

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.29732815327071493
In [582]:
arima_forecast('VESTL', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  61.45
2023-09-20 10:00:00+03:00  62.15
2023-09-20 11:00:00+03:00  61.65
2023-09-20 12:00:00+03:00  61.85
2023-09-20 13:00:00+03:00  61.35
2023-09-20 14:00:00+03:00  61.05
2023-09-20 15:00:00+03:00  60.85
2023-09-20 16:00:00+03:00  60.50
2023-09-20 17:00:00+03:00  59.90
2023-09-20 18:00:00+03:00  59.50
2023-09-21 09:00:00+03:00  59.05
2023-09-21 10:00:00+03:00  58.75
2023-09-21 11:00:00+03:00  59.60
2023-09-21 12:00:00+03:00  59.15
2023-09-21 13:00:00+03:00  59.35
2023-09-21 14:00:00+03:00  60.35
2023-09-21 15:00:00+03:00  60.05
2023-09-21 16:00:00+03:00  60.95
2023-09-21 17:00:00+03:00  61.70
2023-09-21 18:00:00+03:00  61.90
2023-09-22 09:00:00+03:00  62.05
2023-09-22 10:00:00+03:00  62.55
2023-09-22 11:00:00+03:00  63.40
2023-09-22 12:00:00+03:00  63.10
2023-09-22 13:00:00+03:00  63.10
2023-09-22 14:00:00+03:00  63.10
2023-09-22 15:00:00+03:00  62.55
2023-09-22 16:00:00+03:00  64.15
2023-09-22 17:00:00+03:00  64.20
2023-09-22 18:00:00+03:00  64.10
Mean of the first 10 values: price    64.705
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  64.40
2023-09-25 10:00:00+03:00  65.10
2023-09-25 11:00:00+03:00  64.80
2023-09-25 12:00:00+03:00  64.75
2023-09-25 13:00:00+03:00  65.00
2023-09-25 14:00:00+03:00  64.45
2023-09-25 15:00:00+03:00  64.30
2023-09-25 16:00:00+03:00  64.70
2023-09-25 17:00:00+03:00  64.80
2023-09-25 18:00:00+03:00  64.75
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.1733
2018-01-02 11:00:00+03:00  0.3150
2018-01-02 12:00:00+03:00  0.0393
2018-01-02 13:00:00+03:00  0.0000
2018-01-02 14:00:00+03:00  0.3229
...                           ...
2023-09-22 14:00:00+03:00  0.0000
2023-09-22 15:00:00+03:00 -0.5500
2023-09-22 16:00:00+03:00  1.6000
2023-09-22 17:00:00+03:00  0.0500
2023-09-22 18:00:00+03:00 -0.1000

[14226 rows x 1 columns]
ADF Statistic: -17.04161623822715
p-value: 8.188635177351237e-30
Critical Values: {'1%': -3.430811052000141, '5%': -2.8617437641803356, '10%': -2.566878458903999}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood               -3104.699
Date:                Sun, 24 Dec 2023   AIC                           6273.399
Time:                        02:08:31   BIC                           6515.409
Sample:                             0   HQIC                          6353.903
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.6190      0.066      9.352      0.000       0.489       0.749
ar.L2          0.0048      0.004      1.239      0.215      -0.003       0.012
ar.L3          0.0117      0.004      2.857      0.004       0.004       0.020
ar.L4         -0.0082      0.004     -1.856      0.064      -0.017       0.000
ar.L5          0.0018      0.004      0.463      0.643      -0.006       0.010
ar.L6          0.0394      0.004     10.300      0.000       0.032       0.047
ar.L7         -0.0205      0.005     -4.160      0.000      -0.030      -0.011
ar.L8          0.0147      0.004      3.645      0.000       0.007       0.023
ar.L9          0.0320      0.004      7.342      0.000       0.023       0.041
ar.L10        -0.0425      0.005     -9.389      0.000      -0.051      -0.034
ar.L11        -0.0066      0.004     -1.797      0.072      -0.014       0.001
ar.L12        -0.0142      0.005     -2.918      0.004      -0.024      -0.005
ar.L13         0.0051      0.004      1.160      0.246      -0.004       0.014
ar.L14        -0.0630      0.004    -14.078      0.000      -0.072      -0.054
ar.L15         0.0340      0.006      5.486      0.000       0.022       0.046
ar.L16         0.0176      0.004      4.301      0.000       0.010       0.026
ar.L17         0.0131      0.005      2.880      0.004       0.004       0.022
ar.L18        -0.0104      0.005     -2.060      0.039      -0.020      -0.001
ar.L19        -0.0002      0.004     -0.036      0.971      -0.008       0.008
ar.L20         0.0518      0.004     13.638      0.000       0.044       0.059
ar.L21        -0.0500      0.005     -9.547      0.000      -0.060      -0.040
ar.L22        -0.0076      0.005     -1.496      0.135      -0.017       0.002
ar.L23         0.0194      0.005      3.952      0.000       0.010       0.029
ar.L24         0.0121      0.004      2.757      0.006       0.003       0.021
ar.L25        -0.0233      0.005     -5.124      0.000      -0.032      -0.014
ar.L26         0.0109      0.005      2.371      0.018       0.002       0.020
ar.L27        -0.0089      0.004     -2.057      0.040      -0.017      -0.000
ar.L28         0.0110      0.005      2.140      0.032       0.001       0.021
ar.L29         0.0135      0.005      2.654      0.008       0.004       0.023
ar.L30         0.0150      0.004      3.469      0.001       0.007       0.023
ma.L1         -0.6222      0.066     -9.366      0.000      -0.752      -0.492
sigma2         0.0906      0.000    346.291      0.000       0.090       0.091
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):            801231.75
Prob(Q):                              0.99   Prob(JB):                         0.00
Heteroskedasticity (H):              36.06   Skew:                             0.46
Prob(H) (two-sided):                  0.00   Kurtosis:                        39.75
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    64.140910
14228    64.117436
14229    64.067607
14230    64.055273
14231    64.032753
14232    64.064191
14233    64.047754
14234    64.043630
14235    64.063292
14236    63.995251
Name: predicted_mean, dtype: float64
       lower price  upper price
14227    63.550996    64.730825
14228    63.284536    64.950335
14229    63.047130    65.088084
14230    62.872723    65.237822
14231    62.707798    65.357708
14232    62.610236    65.518146
14233    62.466040    65.629469
14234    62.342756    65.744504
14235    62.247398    65.879186
14236    62.062461    65.928041

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.6886839822057449
In [686]:
arima_forecast('YKBNK', 30, 1, 1)

                           price
timestamp                       
2023-09-20 09:00:00+03:00  16.15
2023-09-20 10:00:00+03:00  16.42
2023-09-20 11:00:00+03:00  16.55
2023-09-20 12:00:00+03:00  16.59
2023-09-20 13:00:00+03:00  16.64
2023-09-20 14:00:00+03:00  16.64
2023-09-20 15:00:00+03:00  16.89
2023-09-20 16:00:00+03:00  16.85
2023-09-20 17:00:00+03:00  16.52
2023-09-20 18:00:00+03:00  16.51
2023-09-21 09:00:00+03:00  16.51
2023-09-21 10:00:00+03:00  16.78
2023-09-21 11:00:00+03:00  16.69
2023-09-21 12:00:00+03:00  16.74
2023-09-21 13:00:00+03:00  16.83
2023-09-21 14:00:00+03:00  16.77
2023-09-21 15:00:00+03:00  16.85
2023-09-21 16:00:00+03:00  16.92
2023-09-21 17:00:00+03:00  16.96
2023-09-21 18:00:00+03:00  17.00
2023-09-22 09:00:00+03:00  17.00
2023-09-22 10:00:00+03:00  16.90
2023-09-22 11:00:00+03:00  17.01
2023-09-22 12:00:00+03:00  16.98
2023-09-22 13:00:00+03:00  17.06
2023-09-22 14:00:00+03:00  16.91
2023-09-22 15:00:00+03:00  16.93
2023-09-22 16:00:00+03:00  16.99
2023-09-22 17:00:00+03:00  16.81
2023-09-22 18:00:00+03:00  16.80
Mean of the first 10 values: price    17.015
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  16.89
2023-09-25 10:00:00+03:00  16.90
2023-09-25 11:00:00+03:00  16.91
2023-09-25 12:00:00+03:00  17.02
2023-09-25 13:00:00+03:00  17.09
2023-09-25 14:00:00+03:00  17.02
2023-09-25 15:00:00+03:00  17.02
2023-09-25 16:00:00+03:00  17.12
2023-09-25 17:00:00+03:00  17.08
2023-09-25 18:00:00+03:00  17.10
                            price
timestamp                        
2018-01-02 10:00:00+03:00  0.0169
2018-01-02 11:00:00+03:00  0.0169
2018-01-02 12:00:00+03:00  0.0000
2018-01-02 13:00:00+03:00  0.0000
2018-01-02 14:00:00+03:00  0.0167
...                           ...
2023-09-22 14:00:00+03:00 -0.1500
2023-09-22 15:00:00+03:00  0.0200
2023-09-22 16:00:00+03:00  0.0600
2023-09-22 17:00:00+03:00 -0.1800
2023-09-22 18:00:00+03:00 -0.0100

[14226 rows x 1 columns]
ADF Statistic: -17.782246906761788
p-value: 3.273386116327592e-30
Critical Values: {'1%': -3.43081108450881, '5%': -2.861743778546585, '10%': -2.5668784665510107}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                14227
Model:                ARIMA(30, 1, 1)   Log Likelihood               20865.211
Date:                Sun, 24 Dec 2023   AIC                         -41666.422
Time:                        18:28:01   BIC                         -41424.412
Sample:                             0   HQIC                        -41585.917
                              - 14227                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.0073      1.138      0.006      0.995      -2.224       2.239
ar.L2         -0.0077      0.017     -0.443      0.658      -0.042       0.026
ar.L3          0.0301      0.010      3.072      0.002       0.011       0.049
ar.L4          0.0312      0.034      0.913      0.361      -0.036       0.098
ar.L5          0.0084      0.035      0.238      0.812      -0.061       0.078
ar.L6          0.0181      0.010      1.766      0.077      -0.002       0.038
ar.L7         -0.0183      0.021     -0.882      0.378      -0.059       0.022
ar.L8          0.0029      0.021      0.137      0.891      -0.039       0.045
ar.L9          0.0229      0.005      4.802      0.000       0.014       0.032
ar.L10        -0.0079      0.026     -0.302      0.763      -0.059       0.043
ar.L11        -0.0056      0.010     -0.557      0.578      -0.025       0.014
ar.L12        -0.0177      0.008     -2.200      0.028      -0.034      -0.002
ar.L13        -0.0084      0.020     -0.410      0.682      -0.048       0.032
ar.L14        -0.0125      0.010     -1.233      0.218      -0.032       0.007
ar.L15         0.0007      0.015      0.050      0.960      -0.028       0.029
ar.L16        -0.0074      0.004     -1.703      0.088      -0.016       0.001
ar.L17         0.0200      0.009      2.190      0.029       0.002       0.038
ar.L18         0.0112      0.023      0.487      0.626      -0.034       0.056
ar.L19         0.0077      0.013      0.590      0.555      -0.018       0.033
ar.L20         0.0073      0.009      0.781      0.435      -0.011       0.026
ar.L21        -0.0259      0.009     -2.864      0.004      -0.044      -0.008
ar.L22        -0.0028      0.030     -0.093      0.926      -0.061       0.056
ar.L23         0.0039      0.005      0.795      0.426      -0.006       0.014
ar.L24        -0.0045      0.006     -0.706      0.480      -0.017       0.008
ar.L25        -0.0351      0.006     -5.455      0.000      -0.048      -0.022
ar.L26         0.0131      0.040      0.324      0.746      -0.066       0.092
ar.L27         0.0385      0.016      2.416      0.016       0.007       0.070
ar.L28        -0.0155      0.044     -0.353      0.724      -0.101       0.070
ar.L29         0.0207      0.018      1.130      0.258      -0.015       0.057
ar.L30        -0.0035      0.024     -0.144      0.885      -0.050       0.043
ma.L1          0.0076      1.138      0.007      0.995      -2.224       2.239
sigma2         0.0031   8.44e-06    369.197      0.000       0.003       0.003
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           1623145.19
Prob(Q):                              0.98   Prob(JB):                         0.00
Heteroskedasticity (H):              37.55   Skew:                            -0.33
Prob(H) (two-sided):                  0.00   Kurtosis:                        55.32
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
14227    16.806804
14228    16.798024
14229    16.791736
14230    16.817846
14231    16.801339
14232    16.796330
14233    16.793985
14234    16.792064
14235    16.795939
14236    16.788944
Name: predicted_mean, dtype: float64
       lower price  upper price
14227    16.697404    16.916204
14228    16.642155    16.953893
14229    16.600846    16.982625
14230    16.595777    17.039915
14231    16.550353    17.052326
14232    16.519028    17.073633
14233    16.491851    17.096119
14234    16.467634    17.116494
14235    16.450509    17.141369
14236    16.422826    17.155061

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.23272251923593065
In [699]:
# Define forecast_values_dict outside the function
forecast_values_dict = {}
In [709]:
forecastplusyahoo('THYAO', 30, 1, 3)

                            price short_name
timestamp                                   
2021-12-27 09:00:00+03:00   21.04      THYAO
2021-12-27 10:00:00+03:00   21.54      THYAO
2021-12-27 11:00:00+03:00   21.20      THYAO
2021-12-27 12:00:00+03:00   21.18      THYAO
2021-12-27 13:00:00+03:00   21.14      THYAO
...                           ...        ...
2023-09-22 14:00:00+03:00  228.80      THYAO
2023-09-22 15:00:00+03:00  227.90      THYAO
2023-09-22 16:00:00+03:00  228.20      THYAO
2023-09-22 17:00:00+03:00  226.80      THYAO
2023-09-22 18:00:00+03:00  226.10      THYAO

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for THYAO.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                               price
2021-12-27 06:00:00+00:00  21.040000
2021-12-27 06:30:00+00:00  21.299999
2021-12-27 07:00:00+00:00  21.540000
2021-12-27 07:30:00+00:00  21.160000
2021-12-27 08:00:00+00:00  21.200000
2021-12-27 08:30:00+00:00  21.299999
2021-12-27 09:00:00+00:00  21.180000
2021-12-27 09:30:00+00:00  21.080000
2021-12-27 10:00:00+00:00  21.140000
2021-12-27 10:30:00+00:00  21.120001
2021-12-27 11:00:00+00:00  20.880000
2021-12-27 11:30:00+00:00  20.860001
2021-12-27 12:00:00+00:00  20.860000
2021-12-27 12:30:00+00:00  20.840000
2021-12-27 13:00:00+00:00  21.040000
2021-12-27 13:30:00+00:00  21.299999
2021-12-27 14:00:00+00:00  20.780000
2021-12-27 15:00:00+00:00  20.780000
2021-12-28 06:00:00+00:00  20.860000
2021-12-28 06:30:00+00:00  20.780001
2021-12-28 07:00:00+00:00  21.060000
2021-12-28 07:30:00+00:00  21.059999
2021-12-28 08:00:00+00:00  20.940000
2021-12-28 08:30:00+00:00  20.940001
2021-12-28 09:00:00+00:00  20.980000
2021-12-28 09:30:00+00:00  21.040001
2021-12-28 10:00:00+00:00  20.920000
2021-12-28 10:30:00+00:00  20.780001
2021-12-28 11:00:00+00:00  20.780000
2021-12-28 11:30:00+00:00  20.799999
2021-12-28 12:00:00+00:00  20.780000
2021-12-28 12:30:00+00:00  20.719999
2021-12-28 13:00:00+00:00  20.480000
2021-12-28 13:30:00+00:00  20.180000
2021-12-28 14:00:00+00:00  20.600000
2021-12-28 15:00:00+00:00  20.480000
2021-12-29 06:00:00+00:00  20.080000
2021-12-29 06:30:00+00:00  19.889999
2021-12-29 07:00:00+00:00  20.040000
2021-12-29 07:30:00+00:00  20.360001
2021-12-29 08:00:00+00:00  20.700000
2021-12-29 08:30:00+00:00  20.680000
2021-12-29 09:00:00+00:00  20.880000
2021-12-29 09:30:00+00:00  20.760000
2021-12-29 10:00:00+00:00  20.860000
2021-12-29 10:30:00+00:00  20.760000
2021-12-29 11:00:00+00:00  20.940000
2021-12-29 11:30:00+00:00  21.120001
2021-12-29 12:00:00+00:00  21.100000
2021-12-29 12:30:00+00:00  21.240000
                                price
2023-09-21 10:00:00+00:00  221.400000
2023-09-21 10:30:00+00:00  225.500000
2023-09-21 11:00:00+00:00  227.900000
2023-09-21 11:30:00+00:00  227.600006
2023-09-21 12:00:00+00:00  226.600000
2023-09-21 12:30:00+00:00  229.100006
2023-09-21 13:00:00+00:00  228.600000
2023-09-21 13:30:00+00:00  231.000000
2023-09-21 14:00:00+00:00  232.900000
2023-09-21 14:30:00+00:00  232.899994
2023-09-21 15:00:00+00:00  232.600000
2023-09-22 06:00:00+00:00  233.000000
2023-09-22 06:30:00+00:00  232.800003
2023-09-22 07:00:00+00:00  231.100000
2023-09-22 07:30:00+00:00  231.800003
2023-09-22 08:00:00+00:00  232.200000
2023-09-22 08:30:00+00:00  231.800003
2023-09-22 09:00:00+00:00  231.700000
2023-09-22 09:30:00+00:00  231.199997
2023-09-22 10:00:00+00:00  230.700000
2023-09-22 10:30:00+00:00  229.399994
2023-09-22 11:00:00+00:00  228.800000
2023-09-22 11:30:00+00:00  227.500000
2023-09-22 12:00:00+00:00  227.900000
2023-09-22 12:30:00+00:00  228.199997
2023-09-22 13:00:00+00:00  228.200000
2023-09-22 13:30:00+00:00  227.000000
2023-09-22 14:00:00+00:00  226.800000
2023-09-22 14:30:00+00:00  226.800003
2023-09-22 15:00:00+00:00  226.100000
Mean of the first 10 values: price    229.86
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  227.0
2023-09-25 10:00:00+03:00  226.9
2023-09-25 11:00:00+03:00  228.5
2023-09-25 12:00:00+03:00  229.7
2023-09-25 13:00:00+03:00  231.0
2023-09-25 14:00:00+03:00  229.9
2023-09-25 15:00:00+03:00  229.0
2023-09-25 16:00:00+03:00  231.3
2023-09-25 17:00:00+03:00  232.8
2023-09-25 18:00:00+03:00  232.5
                              price
2018-01-02 10:00:00+03:00  0.220000
2018-01-02 11:00:00+03:00  0.040000
2018-01-02 12:00:00+03:00  0.000000
2018-01-02 13:00:00+03:00  0.010000
2018-01-02 14:00:00+03:00 -0.010000
...                             ...
2023-09-22 13:00:00+00:00  0.000003
2023-09-22 13:30:00+00:00 -1.200000
2023-09-22 14:00:00+00:00 -0.200000
2023-09-22 14:30:00+00:00  0.000003
2023-09-22 15:00:00+00:00 -0.700003

[17889 rows x 1 columns]
ADF Statistic: -21.690645317785734
p-value: 0.0
Critical Values: {'1%': -3.4307165232547536, '5%': -2.8617019893187847, '10%': -2.5668562226754013}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17890
Model:                ARIMA(30, 3, 1)   Log Likelihood              -14439.608
Date:                Sun, 24 Dec 2023   AIC                          28943.217
Time:                        20:43:19   BIC                          29192.556
Sample:                             0   HQIC                         29025.236
                              - 17890                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.9058      0.003   -349.971      0.000      -0.911      -0.901
ar.L2         -0.8825      0.004   -228.635      0.000      -0.890      -0.875
ar.L3         -0.8595      0.006   -155.683      0.000      -0.870      -0.849
ar.L4         -0.8291      0.007   -124.146      0.000      -0.842      -0.816
ar.L5         -0.7964      0.007   -107.091      0.000      -0.811      -0.782
ar.L6         -0.7438      0.008    -89.619      0.000      -0.760      -0.728
ar.L7         -0.7058      0.009    -79.848      0.000      -0.723      -0.689
ar.L8         -0.6878      0.009    -73.613      0.000      -0.706      -0.669
ar.L9         -0.6329      0.010    -64.710      0.000      -0.652      -0.614
ar.L10        -0.5810      0.010    -60.146      0.000      -0.600      -0.562
ar.L11        -0.5507      0.010    -56.829      0.000      -0.570      -0.532
ar.L12        -0.5207      0.010    -53.006      0.000      -0.540      -0.501
ar.L13        -0.4688      0.010    -47.221      0.000      -0.488      -0.449
ar.L14        -0.4295      0.010    -42.458      0.000      -0.449      -0.410
ar.L15        -0.4145      0.010    -40.207      0.000      -0.435      -0.394
ar.L16        -0.3792      0.010    -37.380      0.000      -0.399      -0.359
ar.L17        -0.3290      0.010    -32.460      0.000      -0.349      -0.309
ar.L18        -0.3042      0.010    -30.748      0.000      -0.324      -0.285
ar.L19        -0.2996      0.010    -30.723      0.000      -0.319      -0.280
ar.L20        -0.3101      0.010    -32.102      0.000      -0.329      -0.291
ar.L21        -0.3150      0.010    -31.742      0.000      -0.334      -0.296
ar.L22        -0.3093      0.009    -33.433      0.000      -0.327      -0.291
ar.L23        -0.2874      0.009    -30.584      0.000      -0.306      -0.269
ar.L24        -0.2786      0.009    -30.334      0.000      -0.297      -0.261
ar.L25        -0.2198      0.008    -26.032      0.000      -0.236      -0.203
ar.L26        -0.1508      0.008    -18.147      0.000      -0.167      -0.134
ar.L27        -0.1178      0.008    -15.554      0.000      -0.133      -0.103
ar.L28        -0.0911      0.007    -13.582      0.000      -0.104      -0.078
ar.L29        -0.0556      0.006     -9.915      0.000      -0.067      -0.045
ar.L30        -0.0308      0.004     -7.906      0.000      -0.038      -0.023
ma.L1         -1.0000      0.004   -275.306      0.000      -1.007      -0.993
sigma2         0.2940      0.001    290.720      0.000       0.292       0.296
===================================================================================
Ljung-Box (L1) (Q):                   0.02   Jarque-Bera (JB):           4171612.78
Prob(Q):                              0.89   Prob(JB):                         0.00
Heteroskedasticity (H):              58.08   Skew:                             3.00
Prob(H) (two-sided):                  0.00   Kurtosis:                        77.57
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890    226.131018
17891    226.268528
17892    226.420330
17893    226.608795
17894    226.707874
17895    226.591448
17896    226.554271
17897    226.543665
17898    226.419615
17899    226.231836
Name: predicted_mean, dtype: float64
       lower price  upper price
17890   225.068295   227.193740
17891   224.693158   227.843899
17892   224.441683   228.398977
17893   224.280469   228.937120
17894   224.057825   229.357923
17895   223.635965   229.546930
17896   223.294828   229.813714
17897   222.984245   230.103086
17898   222.570495   230.268736
17899   222.087304   230.376369

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 3.9116086638429937
Weighted Mean Absolute Percentage Error (WMAPE): 1.4844957820835403
In [710]:
forecastplusyahoo('AKBNK', 30, 1, 1)

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00   6.6937      AKBNK
2021-12-27 10:00:00+03:00   6.7991      AKBNK
2021-12-27 11:00:00+03:00   6.8343      AKBNK
2021-12-27 12:00:00+03:00   6.8430      AKBNK
2021-12-27 13:00:00+03:00   6.8255      AKBNK
...                            ...        ...
2023-09-22 14:00:00+03:00  31.0800      AKBNK
2023-09-22 15:00:00+03:00  30.9000      AKBNK
2023-09-22 16:00:00+03:00  31.1400      AKBNK
2023-09-22 17:00:00+03:00  30.8400      AKBNK
2023-09-22 18:00:00+03:00  30.8000      AKBNK

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for AKBNK.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                            price
2021-12-27 06:00:00+00:00  6.6937
2021-12-27 06:30:00+00:00  7.6900
2021-12-27 07:00:00+00:00  6.7991
2021-12-27 07:30:00+00:00  7.7600
2021-12-27 08:00:00+00:00  6.8343
2021-12-27 08:30:00+00:00  7.8100
2021-12-27 09:00:00+00:00  6.8430
2021-12-27 09:30:00+00:00  7.7700
2021-12-27 10:00:00+00:00  6.8255
2021-12-27 10:30:00+00:00  7.7900
2021-12-27 11:00:00+00:00  6.7816
2021-12-27 11:30:00+00:00  7.6800
2021-12-27 12:00:00+00:00  6.7904
2021-12-27 12:30:00+00:00  7.7700
2021-12-27 13:00:00+00:00  6.8343
2021-12-27 13:30:00+00:00  7.8200
2021-12-27 14:00:00+00:00  6.8782
2021-12-27 15:00:00+00:00  6.8782
2021-12-28 06:00:00+00:00  6.8430
2021-12-28 06:30:00+00:00  7.6100
2021-12-28 07:00:00+00:00  6.6850
2021-12-28 07:30:00+00:00  7.5900
2021-12-28 08:00:00+00:00  6.6323
2021-12-28 08:30:00+00:00  7.5300
2021-12-28 09:00:00+00:00  6.6147
2021-12-28 09:30:00+00:00  7.5000
2021-12-28 10:00:00+00:00  6.5884
2021-12-28 10:30:00+00:00  7.5200
2021-12-28 11:00:00+00:00  6.5884
2021-12-28 11:30:00+00:00  7.5100
2021-12-28 12:00:00+00:00  6.6410
2021-12-28 12:30:00+00:00  7.5200
2021-12-28 13:00:00+00:00  6.5708
2021-12-28 13:30:00+00:00  7.4000
2021-12-28 14:00:00+00:00  6.4917
2021-12-28 15:00:00+00:00  6.4653
2021-12-29 06:00:00+00:00  6.3775
2021-12-29 06:30:00+00:00  7.2000
2021-12-29 07:00:00+00:00  6.3599
2021-12-29 07:30:00+00:00  7.3300
2021-12-29 08:00:00+00:00  6.4477
2021-12-29 08:30:00+00:00  7.3300
2021-12-29 09:00:00+00:00  6.4389
2021-12-29 09:30:00+00:00  7.3300
2021-12-29 10:00:00+00:00  6.4389
2021-12-29 10:30:00+00:00  7.2900
2021-12-29 11:00:00+00:00  6.4214
2021-12-29 11:30:00+00:00  7.3600
2021-12-29 12:00:00+00:00  6.4741
2021-12-29 12:30:00+00:00  7.4100
                               price
2023-09-21 10:00:00+00:00  30.680000
2023-09-21 10:30:00+00:00  29.840000
2023-09-21 11:00:00+00:00  30.180000
2023-09-21 11:30:00+00:00  30.059999
2023-09-21 12:00:00+00:00  30.160000
2023-09-21 12:30:00+00:00  30.580000
2023-09-21 13:00:00+00:00  30.660000
2023-09-21 13:30:00+00:00  30.680000
2023-09-21 14:00:00+00:00  31.240000
2023-09-21 14:30:00+00:00  31.240000
2023-09-21 15:00:00+00:00  31.200000
2023-09-22 06:00:00+00:00  31.200000
2023-09-22 06:30:00+00:00  31.139999
2023-09-22 07:00:00+00:00  31.080000
2023-09-22 07:30:00+00:00  31.379999
2023-09-22 08:00:00+00:00  31.260000
2023-09-22 08:30:00+00:00  31.200001
2023-09-22 09:00:00+00:00  31.200000
2023-09-22 09:30:00+00:00  31.379999
2023-09-22 10:00:00+00:00  31.440000
2023-09-22 10:30:00+00:00  31.280001
2023-09-22 11:00:00+00:00  31.080000
2023-09-22 11:30:00+00:00  30.900000
2023-09-22 12:00:00+00:00  30.900000
2023-09-22 12:30:00+00:00  31.340000
2023-09-22 13:00:00+00:00  31.140000
2023-09-22 13:30:00+00:00  31.100000
2023-09-22 14:00:00+00:00  30.840000
2023-09-22 14:30:00+00:00  30.840000
2023-09-22 15:00:00+00:00  30.800000
Mean of the first 10 values: price    31.734
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  30.94
2023-09-25 10:00:00+03:00  31.20
2023-09-25 11:00:00+03:00  31.32
2023-09-25 12:00:00+03:00  31.74
2023-09-25 13:00:00+03:00  31.98
2023-09-25 14:00:00+03:00  31.58
2023-09-25 15:00:00+03:00  31.78
2023-09-25 16:00:00+03:00  32.30
2023-09-25 17:00:00+03:00  32.28
2023-09-25 18:00:00+03:00  32.22
                                  price
2018-01-02 10:00:00+03:00  1.127000e-01
2018-01-02 11:00:00+03:00  3.520000e-02
2018-01-02 12:00:00+03:00 -1.400000e-02
2018-01-02 13:00:00+03:00  2.100000e-02
2018-01-02 14:00:00+03:00  3.530000e-02
...                                 ...
2023-09-22 13:00:00+00:00 -2.000002e-01
2023-09-22 13:30:00+00:00 -3.999962e-02
2023-09-22 14:00:00+00:00 -2.600004e-01
2023-09-22 14:30:00+00:00  1.525879e-07
2023-09-22 15:00:00+00:00 -4.000015e-02

[17890 rows x 1 columns]
ADF Statistic: -19.89541921245133
p-value: 0.0
Critical Values: {'1%': -3.4307164410996687, '5%': -2.8617019530116066, '10%': -2.5668562033496514}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17891
Model:                ARIMA(30, 1, 1)   Log Likelihood                3158.424
Date:                Sun, 24 Dec 2023   AIC                          -6252.847
Time:                        20:46:03   BIC                          -6003.503
Sample:                             0   HQIC                         -6170.827
                              - 17891                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.4546      0.236     -1.927      0.054      -0.917       0.008
ar.L2          0.2564      0.203      1.261      0.207      -0.142       0.655
ar.L3         -0.0086      0.023     -0.377      0.706      -0.053       0.036
ar.L4          0.0245      0.013      1.949      0.051      -0.000       0.049
ar.L5         -0.0301      0.006     -5.303      0.000      -0.041      -0.019
ar.L6          0.0351      0.009      4.053      0.000       0.018       0.052
ar.L7         -0.0321      0.009     -3.732      0.000      -0.049      -0.015
ar.L8          0.0177      0.009      2.061      0.039       0.001       0.035
ar.L9         -0.0271      0.007     -3.698      0.000      -0.042      -0.013
ar.L10         0.0426      0.009      4.844      0.000       0.025       0.060
ar.L11         0.0134      0.009      1.441      0.150      -0.005       0.032
ar.L12        -0.0289      0.009     -3.332      0.001      -0.046      -0.012
ar.L13        -0.0228      0.007     -3.467      0.001      -0.036      -0.010
ar.L14        -0.0483      0.008     -5.816      0.000      -0.065      -0.032
ar.L15        -0.0154      0.015     -1.031      0.303      -0.045       0.014
ar.L16        -0.1679      0.010    -16.427      0.000      -0.188      -0.148
ar.L17        -0.0391      0.044     -0.894      0.371      -0.125       0.047
ar.L18         0.4337      0.027     15.932      0.000       0.380       0.487
ar.L19         0.3392      0.091      3.721      0.000       0.160       0.518
ar.L20        -0.2754      0.117     -2.352      0.019      -0.505      -0.046
ar.L21        -0.1264      0.018     -7.119      0.000      -0.161      -0.092
ar.L22         0.0053      0.037      0.142      0.887      -0.067       0.078
ar.L23        -0.0556      0.015     -3.800      0.000      -0.084      -0.027
ar.L24         0.0099      0.020      0.503      0.615      -0.029       0.049
ar.L25        -0.0301      0.008     -3.776      0.000      -0.046      -0.014
ar.L26         0.0297      0.012      2.573      0.010       0.007       0.052
ar.L27        -0.0293      0.008     -3.908      0.000      -0.044      -0.015
ar.L28         0.0073      0.009      0.805      0.421      -0.011       0.025
ar.L29        -0.0641      0.006    -10.511      0.000      -0.076      -0.052
ar.L30         0.0505      0.017      2.998      0.003       0.017       0.084
ma.L1         -0.4060      0.236     -1.722      0.085      -0.868       0.056
sigma2         0.0411      0.000    247.447      0.000       0.041       0.041
===================================================================================
Ljung-Box (L1) (Q):                   0.15   Jarque-Bera (JB):            320415.70
Prob(Q):                              0.70   Prob(JB):                         0.00
Heteroskedasticity (H):              25.15   Skew:                            -0.71
Prob(H) (two-sided):                  0.00   Kurtosis:                        23.68
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891    30.809788
17892    30.636290
17893    30.884327
17894    30.754408
17895    30.750428
17896    30.682619
17897    30.872461
17898    30.822663
17899    30.871648
17900    30.649573
Name: predicted_mean, dtype: float64
       lower price  upper price
17891    30.412487    31.207090
17892    30.235147    31.037434
17893    30.375712    31.392942
17894    30.235143    31.273673
17895    30.161489    31.339367
17896    30.080930    31.284307
17897    30.212862    31.532060
17898    30.151382    31.493943
17899    30.149050    31.594246
17900    29.916656    31.382489

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 1.0615514510933883
Weighted Mean Absolute Percentage Error (WMAPE): 3.0269725281847215
In [711]:
forecastplusyahoo('ARCLK', 30, 1, 1)

                              price short_name
timestamp                                     
2021-12-27 09:00:00+03:00   49.8145      ARCLK
2021-12-27 10:00:00+03:00   49.6728      ARCLK
2021-12-27 11:00:00+03:00   49.4837      ARCLK
2021-12-27 12:00:00+03:00   48.9639      ARCLK
2021-12-27 13:00:00+03:00   49.3419      ARCLK
...                             ...        ...
2023-09-22 14:00:00+03:00  155.2158      ARCLK
2023-09-22 15:00:00+03:00  155.0192      ARCLK
2023-09-22 16:00:00+03:00  155.5107      ARCLK
2023-09-22 17:00:00+03:00  153.3481      ARCLK
2023-09-22 18:00:00+03:00  154.0362      ARCLK

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for ARCLK.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                               price
2021-12-27 06:00:00+00:00  49.814500
2021-12-27 06:30:00+00:00  52.549999
2021-12-27 07:00:00+00:00  49.672800
2021-12-27 07:30:00+00:00  52.650002
2021-12-27 08:00:00+00:00  49.483700
2021-12-27 08:30:00+00:00  52.200001
2021-12-27 09:00:00+00:00  48.963900
2021-12-27 09:30:00+00:00  51.950001
2021-12-27 10:00:00+00:00  49.341900
2021-12-27 10:30:00+00:00  52.000000
2021-12-27 11:00:00+00:00  48.774700
2021-12-27 11:30:00+00:00  50.849998
2021-12-27 12:00:00+00:00  48.113100
2021-12-27 12:30:00+00:00  50.799999
2021-12-27 13:00:00+00:00  48.302200
2021-12-27 13:30:00+00:00  51.299999
2021-12-27 14:00:00+00:00  48.349400
2021-12-27 15:00:00+00:00  48.113100
2021-12-28 06:00:00+00:00  48.443900
2021-12-28 06:30:00+00:00  50.450001
2021-12-28 07:00:00+00:00  48.443900
2021-12-28 07:30:00+00:00  51.049999
2021-12-28 08:00:00+00:00  48.680300
2021-12-28 08:30:00+00:00  51.250000
2021-12-28 09:00:00+00:00  48.443900
2021-12-28 09:30:00+00:00  51.099998
2021-12-28 10:00:00+00:00  48.113100
2021-12-28 10:30:00+00:00  50.700001
2021-12-28 11:00:00+00:00  47.735000
2021-12-28 11:30:00+00:00  50.599998
2021-12-28 12:00:00+00:00  47.498700
2021-12-28 12:30:00+00:00  50.250000
2021-12-28 13:00:00+00:00  47.356900
2021-12-28 13:30:00+00:00  49.320000
2021-12-28 14:00:00+00:00  46.789800
2021-12-28 15:00:00+00:00  46.827600
2021-12-29 06:00:00+00:00  46.506200
2021-12-29 06:30:00+00:00  48.700001
2021-12-29 07:00:00+00:00  46.506200
2021-12-29 07:30:00+00:00  49.799999
2021-12-29 08:00:00+00:00  47.498700
2021-12-29 08:30:00+00:00  49.980000
2021-12-29 09:00:00+00:00  47.404100
2021-12-29 09:30:00+00:00  50.049999
2021-12-29 10:00:00+00:00  47.356900
2021-12-29 10:30:00+00:00  49.799999
2021-12-29 11:00:00+00:00  47.404100
2021-12-29 11:30:00+00:00  50.200001
2021-12-29 12:00:00+00:00  47.404100
2021-12-29 12:30:00+00:00  50.200001
                                price
2023-09-21 10:00:00+00:00  150.005900
2023-09-21 10:30:00+00:00  154.300003
2023-09-21 11:00:00+00:00  153.937900
2023-09-21 11:30:00+00:00  156.199997
2023-09-21 12:00:00+00:00  153.053200
2023-09-21 12:30:00+00:00  157.100006
2023-09-21 13:00:00+00:00  153.741300
2023-09-21 13:30:00+00:00  158.000000
2023-09-21 14:00:00+00:00  155.510700
2023-09-21 14:30:00+00:00  158.199997
2023-09-21 15:00:00+00:00  155.609000
2023-09-22 06:00:00+00:00  155.707300
2023-09-22 06:30:00+00:00  157.800003
2023-09-22 07:00:00+00:00  154.822600
2023-09-22 07:30:00+00:00  157.699997
2023-09-22 08:00:00+00:00  155.314100
2023-09-22 08:30:00+00:00  158.100006
2023-09-22 09:00:00+00:00  155.314100
2023-09-22 09:30:00+00:00  157.899994
2023-09-22 10:00:00+00:00  155.903900
2023-09-22 10:30:00+00:00  158.199997
2023-09-22 11:00:00+00:00  155.215800
2023-09-22 11:30:00+00:00  156.899994
2023-09-22 12:00:00+00:00  155.019200
2023-09-22 12:30:00+00:00  158.100006
2023-09-22 13:00:00+00:00  155.510700
2023-09-22 13:30:00+00:00  157.699997
2023-09-22 14:00:00+00:00  153.348100
2023-09-22 14:30:00+00:00  156.000000
2023-09-22 15:00:00+00:00  154.036200
Mean of the first 10 values: price    156.59
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  155.6
2023-09-25 10:00:00+03:00  155.5
2023-09-25 11:00:00+03:00  156.3
2023-09-25 12:00:00+03:00  156.3
2023-09-25 13:00:00+03:00  157.6
2023-09-25 14:00:00+03:00  156.4
2023-09-25 15:00:00+03:00  156.5
2023-09-25 16:00:00+03:00  157.3
2023-09-25 17:00:00+03:00  157.2
2023-09-25 18:00:00+03:00  157.2
                              price
2018-01-02 10:00:00+03:00  0.085300
2018-01-02 11:00:00+03:00 -0.119500
2018-01-02 12:00:00+03:00 -0.017100
2018-01-02 13:00:00+03:00  0.000000
2018-01-02 14:00:00+03:00  0.102500
...                             ...
2023-09-22 13:00:00+00:00 -2.589306
2023-09-22 13:30:00+00:00  2.189297
2023-09-22 14:00:00+00:00 -4.351897
2023-09-22 14:30:00+00:00  2.651900
2023-09-22 15:00:00+00:00 -1.963800

[17890 rows x 1 columns]
ADF Statistic: -19.33402242684771
p-value: 0.0
Critical Values: {'1%': -3.4307165027125293, '5%': -2.861701980240464, '10%': -2.5668562178431515}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17891
Model:                ARIMA(30, 1, 1)   Log Likelihood              -18280.318
Date:                Sun, 24 Dec 2023   AIC                          36624.636
Time:                        20:48:49   BIC                          36873.980
Sample:                             0   HQIC                         36706.656
                              - 17891                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.2942      0.005   -255.452      0.000      -1.304      -1.284
ar.L2         -0.3086      0.006    -55.444      0.000      -0.320      -0.298
ar.L3          0.0052      0.006      0.809      0.418      -0.007       0.018
ar.L4         -0.0296      0.006     -4.602      0.000      -0.042      -0.017
ar.L5         -0.0108      0.006     -1.656      0.098      -0.023       0.002
ar.L6         -0.0014      0.006     -0.223      0.823      -0.014       0.011
ar.L7          0.0121      0.007      1.861      0.063      -0.001       0.025
ar.L8          0.0255      0.007      3.728      0.000       0.012       0.039
ar.L9          0.0196      0.007      2.890      0.004       0.006       0.033
ar.L10         0.0107      0.007      1.567      0.117      -0.003       0.024
ar.L11        -0.0338      0.007     -4.893      0.000      -0.047      -0.020
ar.L12        -0.0394      0.007     -5.763      0.000      -0.053      -0.026
ar.L13        -0.0058      0.007     -0.815      0.415      -0.020       0.008
ar.L14         0.0122      0.007      1.707      0.088      -0.002       0.026
ar.L15         0.0310      0.007      4.675      0.000       0.018       0.044
ar.L16        -0.0504      0.007     -7.286      0.000      -0.064      -0.037
ar.L17        -0.0590      0.007     -8.985      0.000      -0.072      -0.046
ar.L18         0.0900      0.006     14.804      0.000       0.078       0.102
ar.L19         0.2844      0.005     53.702      0.000       0.274       0.295
ar.L20         0.0502      0.005      9.181      0.000       0.040       0.061
ar.L21        -0.1591      0.006    -24.995      0.000      -0.172      -0.147
ar.L22        -0.0487      0.007     -6.740      0.000      -0.063      -0.035
ar.L23        -0.0359      0.007     -5.131      0.000      -0.050      -0.022
ar.L24        -0.0159      0.007     -2.292      0.022      -0.029      -0.002
ar.L25        -0.0188      0.007     -2.707      0.007      -0.032      -0.005
ar.L26        -0.0090      0.007     -1.299      0.194      -0.023       0.005
ar.L27        -0.0245      0.007     -3.366      0.001      -0.039      -0.010
ar.L28        -0.0302      0.007     -4.188      0.000      -0.044      -0.016
ar.L29         0.0312      0.007      4.430      0.000       0.017       0.045
ar.L30         0.0830      0.005     16.656      0.000       0.073       0.093
ma.L1          0.8918      0.004    230.359      0.000       0.884       0.899
sigma2         0.4528      0.002    257.954      0.000       0.449       0.456
===================================================================================
Ljung-Box (L1) (Q):                   1.79   Jarque-Bera (JB):            214042.73
Prob(Q):                              0.18   Prob(JB):                         0.00
Heteroskedasticity (H):              78.28   Skew:                             0.92
Prob(H) (two-sided):                  0.00   Kurtosis:                        19.85
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891    155.064428
17892    155.457883
17893    154.139143
17894    156.070727
17895    153.926707
17896    156.178491
17897    153.636560
17898    156.449453
17899    153.923436
17900    156.393710
Name: predicted_mean, dtype: float64
       lower price  upper price
17891   153.745593   156.383263
17892   153.921474   156.994291
17893   152.268029   156.010257
17894   154.004500   158.136954
17895   151.632907   156.220507
17896   153.713212   158.643770
17897   150.983268   156.289851
17898   153.638159   159.260747
17899   150.940554   156.906318
17900   153.265284   159.522136

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 1.9713275245319117
Weighted Mean Absolute Percentage Error (WMAPE): 1.0168957271982435
In [712]:
forecastplusyahoo('ASELS', 30, 1, 3)

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00  10.9361      ASELS
2021-12-27 10:00:00+03:00  11.2048      ASELS
2021-12-27 11:00:00+03:00  11.1252      ASELS
2021-12-27 12:00:00+03:00  11.0655      ASELS
2021-12-27 13:00:00+03:00  11.0754      ASELS
...                            ...        ...
2023-09-22 14:00:00+03:00  40.3000      ASELS
2023-09-22 15:00:00+03:00  40.1600      ASELS
2023-09-22 16:00:00+03:00  40.2400      ASELS
2023-09-22 17:00:00+03:00  40.6400      ASELS
2023-09-22 18:00:00+03:00  40.5200      ASELS

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for ASELS.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                             price
2021-12-27 06:00:00+00:00  10.9361
2021-12-27 06:30:00+00:00  11.1700
2021-12-27 07:00:00+00:00  11.2048
2021-12-27 07:30:00+00:00  11.1500
2021-12-27 08:00:00+00:00  11.1252
2021-12-27 08:30:00+00:00  11.1400
2021-12-27 09:00:00+00:00  11.0655
2021-12-27 09:30:00+00:00  11.0800
2021-12-27 10:00:00+00:00  11.0754
2021-12-27 10:30:00+00:00  11.1000
2021-12-27 11:00:00+00:00  10.9660
2021-12-27 11:30:00+00:00  10.8600
2021-12-27 12:00:00+00:00  10.9063
2021-12-27 12:30:00+00:00  10.9200
2021-12-27 13:00:00+00:00  10.8565
2021-12-27 13:30:00+00:00  11.0300
2021-12-27 14:00:00+00:00  10.8466
2021-12-27 15:00:00+00:00  10.8565
2021-12-28 06:00:00+00:00  10.9560
2021-12-28 06:30:00+00:00  10.9700
2021-12-28 07:00:00+00:00  10.9759
2021-12-28 07:30:00+00:00  10.9600
2021-12-28 08:00:00+00:00  10.8266
2021-12-28 08:30:00+00:00  10.8400
2021-12-28 09:00:00+00:00  10.7570
2021-12-28 09:30:00+00:00  10.8100
2021-12-28 10:00:00+00:00  10.7172
2021-12-28 10:30:00+00:00  10.7500
2021-12-28 11:00:00+00:00  10.6376
2021-12-28 11:30:00+00:00  10.7900
2021-12-28 12:00:00+00:00  10.7172
2021-12-28 12:30:00+00:00  10.7500
2021-12-28 13:00:00+00:00  10.6177
2021-12-28 13:30:00+00:00  10.4600
2021-12-28 14:00:00+00:00  10.4983
2021-12-28 15:00:00+00:00  10.5281
2021-12-29 06:00:00+00:00  10.4485
2021-12-29 06:30:00+00:00  10.3400
2021-12-29 07:00:00+00:00  10.3590
2021-12-29 07:30:00+00:00  10.5300
2021-12-29 08:00:00+00:00  10.6077
2021-12-29 08:30:00+00:00  10.6900
2021-12-29 09:00:00+00:00  10.7072
2021-12-29 09:30:00+00:00  10.7100
2021-12-29 10:00:00+00:00  10.7072
2021-12-29 10:30:00+00:00  10.7000
2021-12-29 11:00:00+00:00  10.7371
2021-12-29 11:30:00+00:00  10.8200
2021-12-29 12:00:00+00:00  10.7570
2021-12-29 12:30:00+00:00  10.8800
                               price
2023-09-21 10:00:00+00:00  37.980000
2023-09-21 10:30:00+00:00  38.540001
2023-09-21 11:00:00+00:00  39.020000
2023-09-21 11:30:00+00:00  38.939999
2023-09-21 12:00:00+00:00  38.980000
2023-09-21 12:30:00+00:00  39.299999
2023-09-21 13:00:00+00:00  39.240000
2023-09-21 13:30:00+00:00  39.599998
2023-09-21 14:00:00+00:00  39.680000
2023-09-21 14:30:00+00:00  39.680000
2023-09-21 15:00:00+00:00  39.860000
2023-09-22 06:00:00+00:00  39.860000
2023-09-22 06:30:00+00:00  40.099998
2023-09-22 07:00:00+00:00  40.940000
2023-09-22 07:30:00+00:00  40.779999
2023-09-22 08:00:00+00:00  40.780000
2023-09-22 08:30:00+00:00  40.700001
2023-09-22 09:00:00+00:00  40.660000
2023-09-22 09:30:00+00:00  40.580002
2023-09-22 10:00:00+00:00  40.520000
2023-09-22 10:30:00+00:00  40.540001
2023-09-22 11:00:00+00:00  40.300000
2023-09-22 11:30:00+00:00  40.080002
2023-09-22 12:00:00+00:00  40.160000
2023-09-22 12:30:00+00:00  40.340000
2023-09-22 13:00:00+00:00  40.240000
2023-09-22 13:30:00+00:00  40.180000
2023-09-22 14:00:00+00:00  40.640000
2023-09-22 14:30:00+00:00  40.639999
2023-09-22 15:00:00+00:00  40.520000
Mean of the first 10 values: price    41.686
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  40.62
2023-09-25 10:00:00+03:00  41.54
2023-09-25 11:00:00+03:00  41.82
2023-09-25 12:00:00+03:00  41.54
2023-09-25 13:00:00+03:00  41.88
2023-09-25 14:00:00+03:00  41.62
2023-09-25 15:00:00+03:00  41.56
2023-09-25 16:00:00+03:00  41.66
2023-09-25 17:00:00+03:00  42.32
2023-09-25 18:00:00+03:00  42.30
                                  price
2018-01-02 10:00:00+03:00  1.440000e-02
2018-01-02 11:00:00+03:00 -1.440000e-02
2018-01-02 12:00:00+03:00  2.890000e-02
2018-01-02 13:00:00+03:00  4.800000e-03
2018-01-02 14:00:00+03:00 -9.600000e-03
...                                 ...
2023-09-22 13:00:00+00:00 -1.000002e-01
2023-09-22 13:30:00+00:00 -5.999969e-02
2023-09-22 14:00:00+00:00  4.599997e-01
2023-09-22 14:30:00+00:00 -6.103516e-07
2023-09-22 15:00:00+00:00 -1.199994e-01

[17780 rows x 1 columns]
ADF Statistic: -20.09626406294903
p-value: 0.0
Critical Values: {'1%': -3.430718776247112, '5%': -2.861702984993756, '10%': -2.5668567526578725}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17781
Model:                ARIMA(30, 3, 1)   Log Likelihood               11009.200
Date:                Sun, 24 Dec 2023   AIC                         -21954.399
Time:                        20:52:13   BIC                         -21705.256
Sample:                             0   HQIC                        -21872.420
                              - 17781                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.9971      0.003   -291.484      0.000      -1.004      -0.990
ar.L2         -0.9346      0.005   -191.695      0.000      -0.944      -0.925
ar.L3         -0.9495      0.006   -146.890      0.000      -0.962      -0.937
ar.L4         -0.8774      0.008   -112.083      0.000      -0.893      -0.862
ar.L5         -0.8668      0.009    -95.139      0.000      -0.885      -0.849
ar.L6         -0.8082      0.010    -78.064      0.000      -0.828      -0.788
ar.L7         -0.7945      0.011    -72.250      0.000      -0.816      -0.773
ar.L8         -0.7365      0.011    -65.281      0.000      -0.759      -0.714
ar.L9         -0.7342      0.012    -62.525      0.000      -0.757      -0.711
ar.L10        -0.6724      0.012    -54.960      0.000      -0.696      -0.648
ar.L11        -0.6179      0.013    -48.928      0.000      -0.643      -0.593
ar.L12        -0.5912      0.012    -48.230      0.000      -0.615      -0.567
ar.L13        -0.5473      0.013    -42.800      0.000      -0.572      -0.522
ar.L14        -0.5137      0.013    -39.469      0.000      -0.539      -0.488
ar.L15        -0.4846      0.013    -37.591      0.000      -0.510      -0.459
ar.L16        -0.4608      0.013    -35.441      0.000      -0.486      -0.435
ar.L17        -0.3988      0.013    -31.417      0.000      -0.424      -0.374
ar.L18        -0.3795      0.013    -30.094      0.000      -0.404      -0.355
ar.L19        -0.3448      0.012    -28.045      0.000      -0.369      -0.321
ar.L20        -0.3468      0.012    -28.926      0.000      -0.370      -0.323
ar.L21        -0.3747      0.012    -31.728      0.000      -0.398      -0.352
ar.L22        -0.3541      0.012    -29.613      0.000      -0.378      -0.331
ar.L23        -0.3126      0.012    -26.375      0.000      -0.336      -0.289
ar.L24        -0.2677      0.012    -22.594      0.000      -0.291      -0.244
ar.L25        -0.2484      0.011    -22.975      0.000      -0.270      -0.227
ar.L26        -0.1837      0.010    -18.230      0.000      -0.203      -0.164
ar.L27        -0.1822      0.009    -19.843      0.000      -0.200      -0.164
ar.L28        -0.1286      0.008    -15.894      0.000      -0.144      -0.113
ar.L29        -0.0979      0.007    -14.841      0.000      -0.111      -0.085
ar.L30        -0.0391      0.005     -8.596      0.000      -0.048      -0.030
ma.L1         -0.9983      0.001   -671.315      0.000      -1.001      -0.995
sigma2         0.0170    4.8e-05    355.057      0.000       0.017       0.017
===================================================================================
Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):           1731880.26
Prob(Q):                              0.91   Prob(JB):                         0.00
Heteroskedasticity (H):              19.63   Skew:                             1.75
Prob(H) (two-sided):                  0.00   Kurtosis:                        51.23
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17781    40.620932
17782    40.720806
17783    40.792644
17784    40.838651
17785    40.844985
17786    40.927508
17787    40.997250
17788    41.085788
17789    41.160636
17790    41.229119
Name: predicted_mean, dtype: float64
       lower price  upper price
17781    40.365083    40.876780
17782    40.358137    41.083476
17783    40.338429    41.246858
17784    40.310140    41.367161
17785    40.242093    41.447876
17786    40.257209    41.597807
17787    40.258364    41.736135
17788    40.282652    41.888925
17789    40.291003    42.030269
17790    40.297158    42.161080

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.8310566241125393
Weighted Mean Absolute Percentage Error (WMAPE): 1.9779651702869407
In [713]:
forecastplusyahoo('BIMAS', 30, 1, 1)

                              price short_name
timestamp                                     
2021-12-27 09:00:00+03:00   62.9265      BIMAS
2021-12-27 10:00:00+03:00   63.4541      BIMAS
2021-12-27 11:00:00+03:00   62.8305      BIMAS
2021-12-27 12:00:00+03:00   62.5908      BIMAS
2021-12-27 13:00:00+03:00   62.4948      BIMAS
...                             ...        ...
2023-09-22 14:00:00+03:00  274.0000      BIMAS
2023-09-22 15:00:00+03:00  273.0000      BIMAS
2023-09-22 16:00:00+03:00  272.4000      BIMAS
2023-09-22 17:00:00+03:00  273.4000      BIMAS
2023-09-22 18:00:00+03:00  273.4000      BIMAS

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for BIMAS.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                               price
2021-12-27 06:00:00+00:00  62.926500
2021-12-27 06:30:00+00:00  65.800003
2021-12-27 07:00:00+00:00  63.454100
2021-12-27 07:30:00+00:00  65.550003
2021-12-27 08:00:00+00:00  62.830500
2021-12-27 08:30:00+00:00  65.449997
2021-12-27 09:00:00+00:00  62.590800
2021-12-27 09:30:00+00:00  65.000000
2021-12-27 10:00:00+00:00  62.494800
2021-12-27 10:30:00+00:00  65.000000
2021-12-27 11:00:00+00:00  61.775300
2021-12-27 11:30:00+00:00  64.150002
2021-12-27 12:00:00+00:00  61.775300
2021-12-27 12:30:00+00:00  64.349998
2021-12-27 13:00:00+00:00  61.727400
2021-12-27 13:30:00+00:00  64.750000
2021-12-27 14:00:00+00:00  61.439600
2021-12-27 15:00:00+00:00  61.295700
2021-12-28 06:00:00+00:00  61.871400
2021-12-28 06:30:00+00:00  64.599998
2021-12-28 07:00:00+00:00  61.823300
2021-12-28 07:30:00+00:00  64.199997
2021-12-28 08:00:00+00:00  61.391600
2021-12-28 08:30:00+00:00  63.650002
2021-12-28 09:00:00+00:00  61.008000
2021-12-28 09:30:00+00:00  63.799999
2021-12-28 10:00:00+00:00  61.151900
2021-12-28 10:30:00+00:00  63.450001
2021-12-28 11:00:00+00:00  60.720200
2021-12-28 11:30:00+00:00  63.900002
2021-12-28 12:00:00+00:00  61.247800
2021-12-28 12:30:00+00:00  63.750000
2021-12-28 13:00:00+00:00  60.624300
2021-12-28 13:30:00+00:00  62.500000
2021-12-28 14:00:00+00:00  60.000700
2021-12-28 15:00:00+00:00  60.048800
2021-12-29 06:00:00+00:00  60.096700
2021-12-29 06:30:00+00:00  62.849998
2021-12-29 07:00:00+00:00  60.576400
2021-12-29 07:30:00+00:00  63.650002
2021-12-29 08:00:00+00:00  61.391600
2021-12-29 08:30:00+00:00  63.900002
2021-12-29 09:00:00+00:00  61.439600
2021-12-29 09:30:00+00:00  64.050003
2021-12-29 10:00:00+00:00  61.439600
2021-12-29 10:30:00+00:00  63.799999
2021-12-29 11:00:00+00:00  61.343700
2021-12-29 11:30:00+00:00  64.199997
2021-12-29 12:00:00+00:00  61.343700
2021-12-29 12:30:00+00:00  63.950001
                                price
2023-09-21 10:00:00+00:00  270.600000
2023-09-21 10:30:00+00:00  273.200012
2023-09-21 11:00:00+00:00  276.000000
2023-09-21 11:30:00+00:00  275.100006
2023-09-21 12:00:00+00:00  273.800000
2023-09-21 12:30:00+00:00  275.600006
2023-09-21 13:00:00+00:00  274.300000
2023-09-21 13:30:00+00:00  274.200012
2023-09-21 14:00:00+00:00  276.000000
2023-09-21 14:30:00+00:00  276.000000
2023-09-21 15:00:00+00:00  275.700000
2023-09-22 06:00:00+00:00  278.000000
2023-09-22 06:30:00+00:00  277.100006
2023-09-22 07:00:00+00:00  274.100000
2023-09-22 07:30:00+00:00  274.200012
2023-09-22 08:00:00+00:00  274.300000
2023-09-22 08:30:00+00:00  275.399994
2023-09-22 09:00:00+00:00  276.200000
2023-09-22 09:30:00+00:00  274.600006
2023-09-22 10:00:00+00:00  275.400000
2023-09-22 10:30:00+00:00  274.500000
2023-09-22 11:00:00+00:00  274.000000
2023-09-22 11:30:00+00:00  272.600006
2023-09-22 12:00:00+00:00  273.000000
2023-09-22 12:30:00+00:00  272.600006
2023-09-22 13:00:00+00:00  272.400000
2023-09-22 13:30:00+00:00  271.799988
2023-09-22 14:00:00+00:00  273.400000
2023-09-22 14:30:00+00:00  273.399994
2023-09-22 15:00:00+00:00  273.400000
Mean of the first 10 values: price    274.68
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  275.0
2023-09-25 10:00:00+03:00  272.1
2023-09-25 11:00:00+03:00  272.9
2023-09-25 12:00:00+03:00  273.9
2023-09-25 13:00:00+03:00  274.2
2023-09-25 14:00:00+03:00  273.9
2023-09-25 15:00:00+03:00  273.7
2023-09-25 16:00:00+03:00  274.8
2023-09-25 17:00:00+03:00  278.6
2023-09-25 18:00:00+03:00  277.7
                              price
2018-01-02 10:00:00+03:00  0.346700
2018-01-02 11:00:00+03:00 -0.122600
2018-01-02 12:00:00+03:00 -0.183300
2018-01-02 13:00:00+03:00  0.142600
2018-01-02 14:00:00+03:00  0.142800
...                             ...
2023-09-22 13:00:00+00:00 -0.200006
2023-09-22 13:30:00+00:00 -0.600012
2023-09-22 14:00:00+00:00  1.600012
2023-09-22 14:30:00+00:00 -0.000006
2023-09-22 15:00:00+00:00  0.000006

[17889 rows x 1 columns]
ADF Statistic: -19.96823456823633
p-value: 0.0
Critical Values: {'1%': -3.4307165232547536, '5%': -2.8617019893187847, '10%': -2.5668562226754013}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17890
Model:                ARIMA(30, 1, 1)   Log Likelihood              -22483.181
Date:                Sun, 24 Dec 2023   AIC                          45030.362
Time:                        20:54:51   BIC                          45279.704
Sample:                             0   HQIC                         45112.382
                              - 17890                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.2601      0.006   -203.316      0.000      -1.272      -1.248
ar.L2         -0.2749      0.006    -48.309      0.000      -0.286      -0.264
ar.L3         -0.0154      0.007     -2.232      0.026      -0.029      -0.002
ar.L4         -0.0441      0.008     -5.527      0.000      -0.060      -0.028
ar.L5         -0.0144      0.008     -1.783      0.075      -0.030       0.001
ar.L6          0.0207      0.007      2.811      0.005       0.006       0.035
ar.L7          0.0443      0.007      6.601      0.000       0.031       0.057
ar.L8          0.0207      0.007      2.846      0.004       0.006       0.035
ar.L9          0.0217      0.007      2.953      0.003       0.007       0.036
ar.L10         0.0483      0.008      6.321      0.000       0.033       0.063
ar.L11         0.0511      0.008      6.633      0.000       0.036       0.066
ar.L12         0.0208      0.007      2.848      0.004       0.006       0.035
ar.L13         0.0213      0.007      3.174      0.002       0.008       0.035
ar.L14         0.0303      0.008      4.020      0.000       0.016       0.045
ar.L15         0.0460      0.008      5.837      0.000       0.031       0.061
ar.L16        -0.0346      0.008     -4.488      0.000      -0.050      -0.019
ar.L17        -0.0903      0.007    -12.344      0.000      -0.105      -0.076
ar.L18         0.0817      0.007     12.544      0.000       0.069       0.094
ar.L19         0.2501      0.005     54.299      0.000       0.241       0.259
ar.L20     -5.643e-05      0.005     -0.011      0.991      -0.010       0.010
ar.L21        -0.1588      0.006    -25.732      0.000      -0.171      -0.147
ar.L22        -0.0542      0.007     -7.555      0.000      -0.068      -0.040
ar.L23        -0.0534      0.008     -6.545      0.000      -0.069      -0.037
ar.L24        -0.0457      0.008     -5.642      0.000      -0.062      -0.030
ar.L25        -0.0326      0.008     -4.069      0.000      -0.048      -0.017
ar.L26        -0.0089      0.009     -1.043      0.297      -0.026       0.008
ar.L27        -0.0175      0.008     -2.183      0.029      -0.033      -0.002
ar.L28        -0.0428      0.008     -5.170      0.000      -0.059      -0.027
ar.L29        -0.0166      0.009     -1.942      0.052      -0.033       0.000
ar.L30         0.0550      0.006      9.161      0.000       0.043       0.067
ma.L1          0.8736      0.005    169.905      0.000       0.864       0.884
sigma2         0.7211      0.003    271.869      0.000       0.716       0.726
===================================================================================
Ljung-Box (L1) (Q):                   0.21   Jarque-Bera (JB):            877251.61
Prob(Q):                              0.65   Prob(JB):                         0.00
Heteroskedasticity (H):              23.85   Skew:                            -0.10
Prob(H) (two-sided):                  0.00   Kurtosis:                        37.31
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890    273.547564
17891    272.900536
17892    272.512197
17893    272.840211
17894    273.016342
17895    273.298569
17896    272.838092
17897    272.998038
17898    273.144972
17899    272.822982
Name: predicted_mean, dtype: float64
       lower price  upper price
17890   271.883226   275.211902
17891   270.947918   274.853153
17892   270.124526   274.899868
17893   270.219428   275.460993
17894   270.095265   275.937418
17895   270.175240   276.421898
17896   269.449960   276.226225
17897   269.414434   276.581642
17898   269.331115   276.958829
17899   268.823025   276.822938

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 2.516333261049285
Weighted Mean Absolute Percentage Error (WMAPE): 0.6904617330843175
In [714]:
forecastplusyahoo('DOHOL', 30, 1, 1)

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00   2.6366      DOHOL
2021-12-27 10:00:00+03:00   2.6736      DOHOL
2021-12-27 11:00:00+03:00   2.6366      DOHOL
2021-12-27 12:00:00+03:00   2.6458      DOHOL
2021-12-27 13:00:00+03:00   2.6458      DOHOL
...                            ...        ...
2023-09-22 14:00:00+03:00  13.0500      DOHOL
2023-09-22 15:00:00+03:00  13.0600      DOHOL
2023-09-22 16:00:00+03:00  13.0700      DOHOL
2023-09-22 17:00:00+03:00  13.0500      DOHOL
2023-09-22 18:00:00+03:00  13.0600      DOHOL

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for DOHOL.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                            price
2021-12-27 06:00:00+00:00  2.6366
2021-12-27 06:30:00+00:00  2.8500
2021-12-27 07:00:00+00:00  2.6736
2021-12-27 07:30:00+00:00  2.8500
2021-12-27 08:00:00+00:00  2.6366
2021-12-27 08:30:00+00:00  2.8500
2021-12-27 09:00:00+00:00  2.6458
2021-12-27 09:30:00+00:00  2.8400
2021-12-27 10:00:00+00:00  2.6458
2021-12-27 10:30:00+00:00  2.8500
2021-12-27 11:00:00+00:00  2.6180
2021-12-27 11:30:00+00:00  2.7900
2021-12-27 12:00:00+00:00  2.6086
2021-12-27 12:30:00+00:00  2.7900
2021-12-27 13:00:00+00:00  2.6086
2021-12-27 13:30:00+00:00  2.8100
2021-12-27 14:00:00+00:00  2.5901
2021-12-27 15:00:00+00:00  2.5809
2021-12-28 06:00:00+00:00  2.6086
2021-12-28 06:30:00+00:00  2.8100
2021-12-28 07:00:00+00:00  2.6366
2021-12-28 07:30:00+00:00  2.8100
2021-12-28 08:00:00+00:00  2.6086
2021-12-28 08:30:00+00:00  2.8000
2021-12-28 09:00:00+00:00  2.5901
2021-12-28 09:30:00+00:00  2.8000
2021-12-28 10:00:00+00:00  2.6086
2021-12-28 10:30:00+00:00  2.7900
2021-12-28 11:00:00+00:00  2.5901
2021-12-28 11:30:00+00:00  2.7900
2021-12-28 12:00:00+00:00  2.5901
2021-12-28 12:30:00+00:00  2.7800
2021-12-28 13:00:00+00:00  2.5715
2021-12-28 13:30:00+00:00  2.7300
2021-12-28 14:00:00+00:00  2.5529
2021-12-28 15:00:00+00:00  2.5437
2021-12-29 06:00:00+00:00  2.5622
2021-12-29 06:30:00+00:00  2.7600
2021-12-29 07:00:00+00:00  2.5529
2021-12-29 07:30:00+00:00  2.7700
2021-12-29 08:00:00+00:00  2.5809
2021-12-29 08:30:00+00:00  2.7700
2021-12-29 09:00:00+00:00  2.5901
2021-12-29 09:30:00+00:00  2.7900
2021-12-29 10:00:00+00:00  2.5901
2021-12-29 10:30:00+00:00  2.7800
2021-12-29 11:00:00+00:00  2.5901
2021-12-29 11:30:00+00:00  2.8000
2021-12-29 12:00:00+00:00  2.5901
2021-12-29 12:30:00+00:00  2.8200
                           price
2023-09-21 10:00:00+00:00  12.87
2023-09-21 10:30:00+00:00  12.95
2023-09-21 11:00:00+00:00  13.03
2023-09-21 11:30:00+00:00  13.00
2023-09-21 12:00:00+00:00  13.00
2023-09-21 12:30:00+00:00  13.13
2023-09-21 13:00:00+00:00  13.09
2023-09-21 13:30:00+00:00  13.28
2023-09-21 14:00:00+00:00  13.28
2023-09-21 14:30:00+00:00  13.28
2023-09-21 15:00:00+00:00  13.28
2023-09-22 06:00:00+00:00  13.28
2023-09-22 06:30:00+00:00  13.28
2023-09-22 07:00:00+00:00  13.25
2023-09-22 07:30:00+00:00  13.31
2023-09-22 08:00:00+00:00  13.32
2023-09-22 08:30:00+00:00  13.31
2023-09-22 09:00:00+00:00  13.33
2023-09-22 09:30:00+00:00  13.28
2023-09-22 10:00:00+00:00  13.32
2023-09-22 10:30:00+00:00  13.04
2023-09-22 11:00:00+00:00  13.05
2023-09-22 11:30:00+00:00  13.00
2023-09-22 12:00:00+00:00  13.06
2023-09-22 12:30:00+00:00  13.09
2023-09-22 13:00:00+00:00  13.07
2023-09-22 13:30:00+00:00  13.03
2023-09-22 14:00:00+00:00  13.05
2023-09-22 14:30:00+00:00  13.05
2023-09-22 15:00:00+00:00  13.06
Mean of the first 10 values: price    13.307
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  13.14
2023-09-25 10:00:00+03:00  13.20
2023-09-25 11:00:00+03:00  13.19
2023-09-25 12:00:00+03:00  13.29
2023-09-25 13:00:00+03:00  13.32
2023-09-25 14:00:00+03:00  13.27
2023-09-25 15:00:00+03:00  13.31
2023-09-25 16:00:00+03:00  13.36
2023-09-25 17:00:00+03:00  13.50
2023-09-25 18:00:00+03:00  13.49
                                  price
2018-01-02 10:00:00+03:00  8.200000e-03
2018-01-02 11:00:00+03:00  0.000000e+00
2018-01-02 12:00:00+03:00  0.000000e+00
2018-01-02 13:00:00+03:00  7.900000e-03
2018-01-02 14:00:00+03:00 -7.900000e-03
...                                 ...
2023-09-22 13:00:00+00:00 -2.000015e-02
2023-09-22 13:30:00+00:00 -4.000027e-02
2023-09-22 14:00:00+00:00  2.000027e-02
2023-09-22 14:30:00+00:00  1.907349e-07
2023-09-22 15:00:00+00:00  9.999809e-03

[17889 rows x 1 columns]
ADF Statistic: -21.651370306616798
p-value: 0.0
Critical Values: {'1%': -3.430716482172607, '5%': -2.861701971163161, '10%': -2.5668562130114436}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17890
Model:                ARIMA(30, 1, 1)   Log Likelihood               23798.523
Date:                Sun, 24 Dec 2023   AIC                         -47533.046
Time:                        20:57:46   BIC                         -47283.704
Sample:                             0   HQIC                        -47451.026
                              - 17890                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.3540      0.076     -4.643      0.000      -0.503      -0.205
ar.L2          0.1837      0.041      4.488      0.000       0.104       0.264
ar.L3         -0.0915      0.008    -10.947      0.000      -0.108      -0.075
ar.L4          0.0559      0.008      7.007      0.000       0.040       0.072
ar.L5         -0.0243      0.006     -3.866      0.000      -0.037      -0.012
ar.L6          0.0451      0.006      8.145      0.000       0.034       0.056
ar.L7         -0.0270      0.007     -4.107      0.000      -0.040      -0.014
ar.L8          0.0366      0.006      6.321      0.000       0.025       0.048
ar.L9         -0.0232      0.006     -3.992      0.000      -0.035      -0.012
ar.L10         0.0592      0.005     11.145      0.000       0.049       0.070
ar.L11        -0.0140      0.007     -2.067      0.039      -0.027      -0.001
ar.L12        -0.0347      0.006     -6.056      0.000      -0.046      -0.023
ar.L13        -0.0035      0.006     -0.583      0.560      -0.015       0.008
ar.L14        -0.0043      0.005     -0.792      0.429      -0.015       0.006
ar.L15         0.0200      0.005      4.181      0.000       0.011       0.029
ar.L16        -0.1278      0.005    -27.402      0.000      -0.137      -0.119
ar.L17        -0.0328      0.010     -3.139      0.002      -0.053      -0.012
ar.L18         0.1839      0.006     33.077      0.000       0.173       0.195
ar.L19         0.1695      0.014     12.389      0.000       0.143       0.196
ar.L20        -0.2146      0.016    -13.604      0.000      -0.246      -0.184
ar.L21        -0.0345      0.014     -2.428      0.015      -0.062      -0.007
ar.L22         0.0050      0.007      0.716      0.474      -0.009       0.019
ar.L23        -0.0699      0.005    -13.196      0.000      -0.080      -0.060
ar.L24         0.0248      0.008      3.177      0.001       0.009       0.040
ar.L25        -0.0182      0.006     -3.294      0.001      -0.029      -0.007
ar.L26         0.0433      0.006      7.670      0.000       0.032       0.054
ar.L27        -0.0583      0.007     -8.444      0.000      -0.072      -0.045
ar.L28         0.0102      0.007      1.403      0.161      -0.004       0.025
ar.L29        -0.0517      0.005    -10.179      0.000      -0.062      -0.042
ar.L30         0.0806      0.007     11.222      0.000       0.066       0.095
ma.L1         -0.1836      0.076     -2.414      0.016      -0.333      -0.035
sigma2         0.0041   1.74e-05    235.623      0.000       0.004       0.004
===================================================================================
Ljung-Box (L1) (Q):                   0.13   Jarque-Bera (JB):            209167.98
Prob(Q):                              0.72   Prob(JB):                         0.00
Heteroskedasticity (H):              64.14   Skew:                             0.46
Prob(H) (two-sided):                  0.00   Kurtosis:                        19.73
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890    13.013064
17891    13.042579
17892    13.029734
17893    13.074533
17894    13.029376
17895    13.059139
17896    13.048819
17897    13.082186
17898    13.033746
17899    13.006131
Name: predicted_mean, dtype: float64
       lower price  upper price
17890    12.887731    13.138398
17891    12.904494    13.180665
17892    12.856359    13.203110
17893    12.889587    13.259479
17894    12.818890    13.239862
17895    12.837575    13.280704
17896    12.805520    13.292119
17897    12.828866    13.335506
17898    12.760924    13.306568
17899    12.724237    13.288025

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.28962864777279757
Weighted Mean Absolute Percentage Error (WMAPE): 1.9919527840870335
In [717]:
forecastplusyahoo('EKGYO', 30, 1, 3)

                            price short_name
timestamp                                   
2021-12-27 09:00:00+03:00  2.0714      EKGYO
2021-12-27 10:00:00+03:00  2.0993      EKGYO
2021-12-27 11:00:00+03:00  2.0993      EKGYO
2021-12-27 12:00:00+03:00  2.0901      EKGYO
2021-12-27 13:00:00+03:00  2.0901      EKGYO
...                           ...        ...
2023-09-22 14:00:00+03:00  7.9100      EKGYO
2023-09-22 15:00:00+03:00  7.8900      EKGYO
2023-09-22 16:00:00+03:00  7.8900      EKGYO
2023-09-22 17:00:00+03:00  7.9000      EKGYO
2023-09-22 18:00:00+03:00  7.9100      EKGYO

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for EKGYO.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                            price
2021-12-27 06:00:00+00:00  2.0714
2021-12-27 06:30:00+00:00  2.2400
2021-12-27 07:00:00+00:00  2.0993
2021-12-27 07:30:00+00:00  2.2300
2021-12-27 08:00:00+00:00  2.0993
2021-12-27 08:30:00+00:00  2.2400
2021-12-27 09:00:00+00:00  2.0901
2021-12-27 09:30:00+00:00  2.2400
2021-12-27 10:00:00+00:00  2.0901
2021-12-27 10:30:00+00:00  2.2300
2021-12-27 11:00:00+00:00  2.0620
2021-12-27 11:30:00+00:00  2.2100
2021-12-27 12:00:00+00:00  2.0807
2021-12-27 12:30:00+00:00  2.2200
2021-12-27 13:00:00+00:00  2.0901
2021-12-27 13:30:00+00:00  2.2500
2021-12-27 14:00:00+00:00  2.0714
2021-12-27 15:00:00+00:00  2.0714
2021-12-28 06:00:00+00:00  2.1086
2021-12-28 06:30:00+00:00  2.2800
2021-12-28 07:00:00+00:00  2.1460
2021-12-28 07:30:00+00:00  2.3100
2021-12-28 08:00:00+00:00  2.1273
2021-12-28 08:30:00+00:00  2.2400
2021-12-28 09:00:00+00:00  2.0901
2021-12-28 09:30:00+00:00  2.2400
2021-12-28 10:00:00+00:00  2.0901
2021-12-28 10:30:00+00:00  2.2300
2021-12-28 11:00:00+00:00  2.0620
2021-12-28 11:30:00+00:00  2.2200
2021-12-28 12:00:00+00:00  2.0807
2021-12-28 12:30:00+00:00  2.2300
2021-12-28 13:00:00+00:00  2.0714
2021-12-28 13:30:00+00:00  2.2000
2021-12-28 14:00:00+00:00  2.0527
2021-12-28 15:00:00+00:00  2.0527
2021-12-29 06:00:00+00:00  2.0620
2021-12-29 06:30:00+00:00  2.1800
2021-12-29 07:00:00+00:00  2.0247
2021-12-29 07:30:00+00:00  2.1900
2021-12-29 08:00:00+00:00  2.0247
2021-12-29 08:30:00+00:00  2.1800
2021-12-29 09:00:00+00:00  2.0247
2021-12-29 09:30:00+00:00  2.1700
2021-12-29 10:00:00+00:00  2.0341
2021-12-29 10:30:00+00:00  2.1800
2021-12-29 11:00:00+00:00  2.0341
2021-12-29 11:30:00+00:00  2.1900
2021-12-29 12:00:00+00:00  2.0620
2021-12-29 12:30:00+00:00  2.2200
                           price
2023-09-21 10:00:00+00:00   7.80
2023-09-21 10:30:00+00:00   7.86
2023-09-21 11:00:00+00:00   7.92
2023-09-21 11:30:00+00:00   7.88
2023-09-21 12:00:00+00:00   7.88
2023-09-21 12:30:00+00:00   7.92
2023-09-21 13:00:00+00:00   7.91
2023-09-21 13:30:00+00:00   8.01
2023-09-21 14:00:00+00:00   8.01
2023-09-21 14:30:00+00:00   8.01
2023-09-21 15:00:00+00:00   8.03
2023-09-22 06:00:00+00:00   8.03
2023-09-22 06:30:00+00:00   8.05
2023-09-22 07:00:00+00:00   8.02
2023-09-22 07:30:00+00:00   8.05
2023-09-22 08:00:00+00:00   8.05
2023-09-22 08:30:00+00:00   8.03
2023-09-22 09:00:00+00:00   8.03
2023-09-22 09:30:00+00:00   8.00
2023-09-22 10:00:00+00:00   7.98
2023-09-22 10:30:00+00:00   7.95
2023-09-22 11:00:00+00:00   7.91
2023-09-22 11:30:00+00:00   7.88
2023-09-22 12:00:00+00:00   7.89
2023-09-22 12:30:00+00:00   7.90
2023-09-22 13:00:00+00:00   7.89
2023-09-22 13:30:00+00:00   7.89
2023-09-22 14:00:00+00:00   7.90
2023-09-22 14:30:00+00:00   7.90
2023-09-22 15:00:00+00:00   7.91
Mean of the first 10 values: price    8.061
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00   7.94
2023-09-25 10:00:00+03:00   8.08
2023-09-25 11:00:00+03:00   8.08
2023-09-25 12:00:00+03:00   8.06
2023-09-25 13:00:00+03:00   8.05
2023-09-25 14:00:00+03:00   8.00
2023-09-25 15:00:00+03:00   7.99
2023-09-25 16:00:00+03:00   8.05
2023-09-25 17:00:00+03:00   8.18
2023-09-25 18:00:00+03:00   8.18
                                  price
2018-01-02 10:00:00+03:00  2.460000e-02
2018-01-02 11:00:00+03:00 -8.100000e-03
2018-01-02 12:00:00+03:00  8.100000e-03
2018-01-02 13:00:00+03:00  0.000000e+00
2018-01-02 14:00:00+03:00  8.100000e-03
...                                 ...
2023-09-22 13:00:00+00:00 -1.000010e-02
2023-09-22 13:30:00+00:00 -1.335144e-07
2023-09-22 14:00:00+00:00  1.000013e-02
2023-09-22 14:30:00+00:00  9.536743e-08
2023-09-22 15:00:00+00:00  9.999905e-03

[17890 rows x 1 columns]
ADF Statistic: -19.281107921662084
p-value: 0.0
Critical Values: {'1%': -3.4307165027125293, '5%': -2.861701980240464, '10%': -2.5668562178431515}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17891
Model:                ARIMA(30, 3, 1)   Log Likelihood               28040.755
Date:                Sun, 24 Dec 2023   AIC                         -56017.509
Time:                        21:08:49   BIC                         -55768.169
Sample:                             0   HQIC                        -55935.490
                              - 17891                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.4811      0.004   -418.252      0.000      -1.488      -1.474
ar.L2         -1.3162      0.007   -180.997      0.000      -1.330      -1.302
ar.L3         -1.3883      0.010   -133.806      0.000      -1.409      -1.368
ar.L4         -1.3749      0.013   -105.506      0.000      -1.400      -1.349
ar.L5         -1.4124      0.015    -93.288      0.000      -1.442      -1.383
ar.L6         -1.3409      0.017    -78.150      0.000      -1.374      -1.307
ar.L7         -1.3243      0.019    -69.915      0.000      -1.361      -1.287
ar.L8         -1.2535      0.020    -61.618      0.000      -1.293      -1.214
ar.L9         -1.1992      0.021    -55.918      0.000      -1.241      -1.157
ar.L10        -1.1232      0.023    -49.913      0.000      -1.167      -1.079
ar.L11        -1.0407      0.023    -44.411      0.000      -1.087      -0.995
ar.L12        -0.9706      0.024    -40.421      0.000      -1.018      -0.924
ar.L13        -0.8889      0.024    -36.641      0.000      -0.936      -0.841
ar.L14        -0.8977      0.024    -37.669      0.000      -0.944      -0.851
ar.L15        -0.8638      0.024    -36.438      0.000      -0.910      -0.817
ar.L16        -0.9658      0.024    -41.008      0.000      -1.012      -0.920
ar.L17        -1.0634      0.024    -45.193      0.000      -1.110      -1.017
ar.L18        -0.7880      0.024    -33.236      0.000      -0.834      -0.742
ar.L19        -0.5851      0.023    -25.420      0.000      -0.630      -0.540
ar.L20        -0.7642      0.022    -34.464      0.000      -0.808      -0.721
ar.L21        -0.7541      0.022    -35.004      0.000      -0.796      -0.712
ar.L22        -0.6702      0.021    -32.097      0.000      -0.711      -0.629
ar.L23        -0.5992      0.020    -29.795      0.000      -0.639      -0.560
ar.L24        -0.5620      0.019    -29.952      0.000      -0.599      -0.525
ar.L25        -0.5398      0.017    -30.876      0.000      -0.574      -0.506
ar.L26        -0.3453      0.016    -21.402      0.000      -0.377      -0.314
ar.L27        -0.1866      0.014    -13.036      0.000      -0.215      -0.159
ar.L28        -0.0397      0.012     -3.357      0.001      -0.063      -0.017
ar.L29        -0.0120      0.009     -1.296      0.195      -0.030       0.006
ar.L30         0.0628      0.005     12.558      0.000       0.053       0.073
ma.L1         -0.9545      0.002   -434.894      0.000      -0.959      -0.950
sigma2         0.0025   9.16e-06    274.313      0.000       0.002       0.003
===================================================================================
Ljung-Box (L1) (Q):                  33.07   Jarque-Bera (JB):            244783.49
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):              32.23   Skew:                             0.67
Prob(H) (two-sided):                  0.00   Kurtosis:                        21.07
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891    7.907070
17892    7.900599
17893    7.900568
17894    7.922457
17895    7.917572
17896    7.938448
17897    7.929075
17898    7.943442
17899    7.930265
17900    7.926018
Name: predicted_mean, dtype: float64
       lower price  upper price
17891     7.808801     8.005340
17892     7.787759     8.013439
17893     7.751939     8.049197
17894     7.759339     8.085574
17895     7.727748     8.107396
17896     7.734312     8.142583
17897     7.698882     8.159267
17898     7.697483     8.189400
17899     7.657439     8.203091
17900     7.634968     8.217068

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.1573689183058474
Weighted Mean Absolute Percentage Error (WMAPE): 1.7307715709708829
In [724]:
forecastplusyahoo('EREGL', 30, 1, 3)

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00  25.0042      EREGL
2021-12-27 10:00:00+03:00  25.6008      EREGL
2021-12-27 11:00:00+03:00  25.4955      EREGL
2021-12-27 12:00:00+03:00  25.3902      EREGL
2021-12-27 13:00:00+03:00  25.5130      EREGL
...                            ...        ...
2023-09-22 14:00:00+03:00  42.8400      EREGL
2023-09-22 15:00:00+03:00  43.0600      EREGL
2023-09-22 16:00:00+03:00  43.2200      EREGL
2023-09-22 17:00:00+03:00  43.1800      EREGL
2023-09-22 18:00:00+03:00  43.2800      EREGL

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for EREGL.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                               price
2021-12-27 06:00:00+00:00  25.004200
2021-12-27 06:30:00+00:00  29.120001
2021-12-27 07:00:00+00:00  25.600800
2021-12-27 07:30:00+00:00  29.020000
2021-12-27 08:00:00+00:00  25.495500
2021-12-27 08:30:00+00:00  29.020000
2021-12-27 09:00:00+00:00  25.390200
2021-12-27 09:30:00+00:00  28.900000
2021-12-27 10:00:00+00:00  25.513000
2021-12-27 10:30:00+00:00  29.020000
2021-12-27 11:00:00+00:00  25.337600
2021-12-27 11:30:00+00:00  28.600000
2021-12-27 12:00:00+00:00  25.372600
2021-12-27 12:30:00+00:00  28.879999
2021-12-27 13:00:00+00:00  25.407700
2021-12-27 13:30:00+00:00  29.080000
2021-12-27 14:00:00+00:00  25.197200
2021-12-27 15:00:00+00:00  25.197200
2021-12-28 06:00:00+00:00  25.513000
2021-12-28 06:30:00+00:00  28.879999
2021-12-28 07:00:00+00:00  25.425300
2021-12-28 07:30:00+00:00  28.940001
2021-12-28 08:00:00+00:00  25.284900
2021-12-28 08:30:00+00:00  28.799999
2021-12-28 09:00:00+00:00  25.267400
2021-12-28 09:30:00+00:00  28.920000
2021-12-28 10:00:00+00:00  25.320000
2021-12-28 10:30:00+00:00  28.760000
2021-12-28 11:00:00+00:00  25.021700
2021-12-28 11:30:00+00:00  28.719999
2021-12-28 12:00:00+00:00  25.056800
2021-12-28 12:30:00+00:00  28.540001
2021-12-28 13:00:00+00:00  25.039300
2021-12-28 13:30:00+00:00  27.920000
2021-12-28 14:00:00+00:00  24.460200
2021-12-28 15:00:00+00:00  24.407600
2021-12-29 06:00:00+00:00  24.407600
2021-12-29 06:30:00+00:00  27.940001
2021-12-29 07:00:00+00:00  24.951500
2021-12-29 07:30:00+00:00  28.700001
2021-12-29 08:00:00+00:00  25.249800
2021-12-29 08:30:00+00:00  28.719999
2021-12-29 09:00:00+00:00  25.232300
2021-12-29 09:30:00+00:00  28.860001
2021-12-29 10:00:00+00:00  25.477900
2021-12-29 10:30:00+00:00  28.820000
2021-12-29 11:00:00+00:00  25.267400
2021-12-29 11:30:00+00:00  28.980000
2021-12-29 12:00:00+00:00  25.477900
2021-12-29 12:30:00+00:00  29.219999
                               price
2023-09-21 10:00:00+00:00  40.420000
2023-09-21 10:30:00+00:00  40.840000
2023-09-21 11:00:00+00:00  41.260000
2023-09-21 11:30:00+00:00  41.180000
2023-09-21 12:00:00+00:00  41.100000
2023-09-21 12:30:00+00:00  41.340000
2023-09-21 13:00:00+00:00  41.280000
2023-09-21 13:30:00+00:00  41.480000
2023-09-21 14:00:00+00:00  41.720000
2023-09-21 14:30:00+00:00  41.720001
2023-09-21 15:00:00+00:00  41.740000
2023-09-22 06:00:00+00:00  41.840000
2023-09-22 06:30:00+00:00  41.900002
2023-09-22 07:00:00+00:00  42.080000
2023-09-22 07:30:00+00:00  42.080002
2023-09-22 08:00:00+00:00  42.180000
2023-09-22 08:30:00+00:00  42.299999
2023-09-22 09:00:00+00:00  42.320000
2023-09-22 09:30:00+00:00  42.139999
2023-09-22 10:00:00+00:00  42.140000
2023-09-22 10:30:00+00:00  43.000000
2023-09-22 11:00:00+00:00  42.840000
2023-09-22 11:30:00+00:00  42.840000
2023-09-22 12:00:00+00:00  43.060000
2023-09-22 12:30:00+00:00  43.180000
2023-09-22 13:00:00+00:00  43.220000
2023-09-22 13:30:00+00:00  43.119999
2023-09-22 14:00:00+00:00  43.180000
2023-09-22 14:30:00+00:00  43.180000
2023-09-22 15:00:00+00:00  43.280000
Mean of the first 10 values: price    45.784
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  43.44
2023-09-25 10:00:00+03:00  43.98
2023-09-25 11:00:00+03:00  45.16
2023-09-25 12:00:00+03:00  45.74
2023-09-25 13:00:00+03:00  46.80
2023-09-25 14:00:00+03:00  46.68
2023-09-25 15:00:00+03:00  46.60
2023-09-25 16:00:00+03:00  46.74
2023-09-25 17:00:00+03:00  46.38
2023-09-25 18:00:00+03:00  46.32
                                  price
2018-01-02 10:00:00+03:00  1.780000e-02
2018-01-02 11:00:00+03:00 -2.370000e-02
2018-01-02 12:00:00+03:00  0.000000e+00
2018-01-02 13:00:00+03:00  1.180000e-02
2018-01-02 14:00:00+03:00  2.370000e-02
...                                 ...
2023-09-22 13:00:00+00:00  3.999969e-02
2023-09-22 13:30:00+00:00 -1.000011e-01
2023-09-22 14:00:00+00:00  6.000107e-02
2023-09-22 14:30:00+00:00  3.051758e-07
2023-09-22 15:00:00+00:00  9.999969e-02

[17889 rows x 1 columns]
ADF Statistic: -21.727947513805333
p-value: 0.0
Critical Values: {'1%': -3.4307165232547536, '5%': -2.8617019893187847, '10%': -2.5668562226754013}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17890
Model:                ARIMA(30, 3, 1)   Log Likelihood               -1053.501
Date:                Sun, 24 Dec 2023   AIC                           2171.002
Time:                        21:28:11   BIC                           2420.340
Sample:                             0   HQIC                          2253.021
                              - 17890                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.3765      0.003   -457.849      0.000      -1.382      -1.371
ar.L2         -1.2377      0.006   -190.941      0.000      -1.250      -1.225
ar.L3         -1.3343      0.010   -140.370      0.000      -1.353      -1.316
ar.L4         -1.2429      0.012    -99.967      0.000      -1.267      -1.219
ar.L5         -1.3181      0.015    -89.838      0.000      -1.347      -1.289
ar.L6         -1.2403      0.017    -74.212      0.000      -1.273      -1.208
ar.L7         -1.2283      0.018    -66.986      0.000      -1.264      -1.192
ar.L8         -1.1791      0.020    -59.949      0.000      -1.218      -1.141
ar.L9         -1.1382      0.021    -54.670      0.000      -1.179      -1.097
ar.L10        -1.0907      0.022    -50.436      0.000      -1.133      -1.048
ar.L11        -1.0390      0.022    -46.606      0.000      -1.083      -0.995
ar.L12        -1.0399      0.023    -45.935      0.000      -1.084      -0.996
ar.L13        -0.9634      0.023    -41.410      0.000      -1.009      -0.918
ar.L14        -0.9213      0.024    -39.074      0.000      -0.967      -0.875
ar.L15        -0.8872      0.024    -37.622      0.000      -0.933      -0.841
ar.L16        -0.9493      0.024    -40.130      0.000      -0.996      -0.903
ar.L17        -1.0714      0.024    -45.159      0.000      -1.118      -1.025
ar.L18        -0.4291      0.024    -17.970      0.000      -0.476      -0.382
ar.L19        -0.3066      0.023    -13.381      0.000      -0.351      -0.262
ar.L20        -0.5088      0.022    -23.356      0.000      -0.552      -0.466
ar.L21        -0.4655      0.021    -22.257      0.000      -0.507      -0.425
ar.L22        -0.5128      0.020    -25.907      0.000      -0.552      -0.474
ar.L23        -0.4007      0.019    -21.209      0.000      -0.438      -0.364
ar.L24        -0.3859      0.018    -22.018      0.000      -0.420      -0.352
ar.L25        -0.3064      0.016    -19.106      0.000      -0.338      -0.275
ar.L26        -0.2431      0.015    -16.478      0.000      -0.272      -0.214
ar.L27        -0.1774      0.013    -13.334      0.000      -0.204      -0.151
ar.L28        -0.1202      0.011    -11.122      0.000      -0.141      -0.099
ar.L29        -0.0733      0.009     -8.431      0.000      -0.090      -0.056
ar.L30         0.0044      0.005      0.928      0.354      -0.005       0.014
ma.L1         -0.9499      0.002   -433.585      0.000      -0.954      -0.946
sigma2         0.0657      0.000    397.286      0.000       0.065       0.066
===================================================================================
Ljung-Box (L1) (Q):                   0.11   Jarque-Bera (JB):           2064099.53
Prob(Q):                              0.74   Prob(JB):                         0.00
Heteroskedasticity (H):              23.47   Skew:                             0.37
Prob(H) (two-sided):                  0.00   Kurtosis:                        55.62
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890    43.422714
17891    43.675701
17892    43.681565
17893    43.888397
17894    44.031904
17895    44.185842
17896    44.049971
17897    44.147177
17898    44.822329
17899    44.707659
Name: predicted_mean, dtype: float64
       lower price  upper price
17890    42.920310    43.925118
17891    43.069945    44.281457
17892    42.899235    44.463894
17893    43.014276    44.762517
17894    43.005360    45.058449
17895    43.074791    45.296893
17896    42.792281    45.307660
17897    42.795792    45.498561
17898    43.324663    46.319996
17899    43.105414    46.309904

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 1.9452928352152923
Weighted Mean Absolute Percentage Error (WMAPE): 4.124983996856018
In [719]:
forecastplusyahoo('FROTO', 30, 1, 1)

                              price short_name
timestamp                                     
2021-12-27 09:00:00+03:00  216.8891      FROTO
2021-12-27 10:00:00+03:00  225.8110      FROTO
2021-12-27 11:00:00+03:00  223.8482      FROTO
2021-12-27 12:00:00+03:00  221.4393      FROTO
2021-12-27 13:00:00+03:00  221.6177      FROTO
...                             ...        ...
2023-09-22 14:00:00+03:00  810.1695      FROTO
2023-09-22 15:00:00+03:00  808.0466      FROTO
2023-09-22 16:00:00+03:00  809.2045      FROTO
2023-09-22 17:00:00+03:00  812.4853      FROTO
2023-09-22 18:00:00+03:00  809.9765      FROTO

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for FROTO.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                                price
2021-12-27 06:00:00+00:00  216.889100
2021-12-27 06:30:00+00:00  250.199997
2021-12-27 07:00:00+00:00  225.811000
2021-12-27 07:30:00+00:00  251.100006
2021-12-27 08:00:00+00:00  223.848200
2021-12-27 08:30:00+00:00  249.699997
2021-12-27 09:00:00+00:00  221.439300
2021-12-27 09:30:00+00:00  247.300003
2021-12-27 10:00:00+00:00  221.617700
2021-12-27 10:30:00+00:00  247.500000
2021-12-27 11:00:00+00:00  217.959700
2021-12-27 11:30:00+00:00  242.000000
2021-12-27 12:00:00+00:00  218.584400
2021-12-27 12:30:00+00:00  244.000000
2021-12-27 13:00:00+00:00  218.673500
2021-12-27 13:30:00+00:00  246.600006
2021-12-27 14:00:00+00:00  218.495200
2021-12-27 15:00:00+00:00  217.781400
2021-12-28 06:00:00+00:00  219.922500
2021-12-28 06:30:00+00:00  245.500000
2021-12-28 07:00:00+00:00  219.565700
2021-12-28 07:30:00+00:00  243.899994
2021-12-28 08:00:00+00:00  216.889100
2021-12-28 08:30:00+00:00  243.699997
2021-12-28 09:00:00+00:00  216.532300
2021-12-28 09:30:00+00:00  242.699997
2021-12-28 10:00:00+00:00  217.513700
2021-12-28 10:30:00+00:00  241.699997
2021-12-28 11:00:00+00:00  214.301800
2021-12-28 11:30:00+00:00  241.899994
2021-12-28 12:00:00+00:00  216.621500
2021-12-28 12:30:00+00:00  241.899994
2021-12-28 13:00:00+00:00  214.480300
2021-12-28 13:30:00+00:00  237.399994
2021-12-28 14:00:00+00:00  208.948700
2021-12-28 15:00:00+00:00  212.428200
2021-12-29 06:00:00+00:00  215.818600
2021-12-29 06:30:00+00:00  239.300003
2021-12-29 07:00:00+00:00  213.588100
2021-12-29 07:30:00+00:00  241.500000
2021-12-29 08:00:00+00:00  216.175400
2021-12-29 08:30:00+00:00  243.199997
2021-12-29 09:00:00+00:00  217.602900
2021-12-29 09:30:00+00:00  242.500000
2021-12-29 10:00:00+00:00  216.443100
2021-12-29 10:30:00+00:00  240.800003
2021-12-29 11:00:00+00:00  216.264600
2021-12-29 11:30:00+00:00  244.000000
2021-12-29 12:00:00+00:00  217.959700
2021-12-29 12:30:00+00:00  244.800003
                                price
2023-09-21 10:00:00+00:00  795.598800
2023-09-21 10:30:00+00:00  829.799988
2023-09-21 11:00:00+00:00  807.564100
2023-09-21 11:30:00+00:00  835.500000
2023-09-21 12:00:00+00:00  803.897300
2023-09-21 12:30:00+00:00  840.299988
2023-09-21 13:00:00+00:00  806.985100
2023-09-21 13:30:00+00:00  844.599976
2023-09-21 14:00:00+00:00  819.529400
2023-09-21 14:30:00+00:00  849.299988
2023-09-21 15:00:00+00:00  819.722400
2023-09-22 06:00:00+00:00  819.722400
2023-09-22 06:30:00+00:00  848.700012
2023-09-22 07:00:00+00:00  815.476700
2023-09-22 07:30:00+00:00  847.299988
2023-09-22 08:00:00+00:00  816.731100
2023-09-22 08:30:00+00:00  850.200012
2023-09-22 09:00:00+00:00  818.661000
2023-09-22 09:30:00+00:00  846.099976
2023-09-22 10:00:00+00:00  815.090700
2023-09-22 10:30:00+00:00  841.799988
2023-09-22 11:00:00+00:00  810.169500
2023-09-22 11:30:00+00:00  835.799988
2023-09-22 12:00:00+00:00  808.046600
2023-09-22 12:30:00+00:00  839.500000
2023-09-22 13:00:00+00:00  809.204500
2023-09-22 13:30:00+00:00  836.799988
2023-09-22 14:00:00+00:00  812.485300
2023-09-22 14:30:00+00:00  842.000000
2023-09-22 15:00:00+00:00  809.976500
Mean of the first 10 values: price    812.85202
dtype: float64
                              price
timestamp                          
2023-09-25 09:00:00+03:00  813.8363
2023-09-25 10:00:00+03:00  804.9587
2023-09-25 11:00:00+03:00  806.6957
2023-09-25 12:00:00+03:00  812.0994
2023-09-25 13:00:00+03:00  811.1344
2023-09-25 14:00:00+03:00  805.7307
2023-09-25 15:00:00+03:00  809.3010
2023-09-25 16:00:00+03:00  823.9682
2023-09-25 17:00:00+03:00  820.3979
2023-09-25 18:00:00+03:00  820.3979
                               price
2018-01-02 10:00:00+03:00   0.209400
2018-01-02 11:00:00+03:00   0.314200
2018-01-02 12:00:00+03:00   0.174400
2018-01-02 13:00:00+03:00  -0.139500
2018-01-02 14:00:00+03:00   0.279100
...                              ...
2023-09-22 13:00:00+00:00 -30.295500
2023-09-22 13:30:00+00:00  27.595488
2023-09-22 14:00:00+00:00 -24.314688
2023-09-22 14:30:00+00:00  29.514700
2023-09-22 15:00:00+00:00 -32.023500

[17888 rows x 1 columns]
ADF Statistic: -21.61039641010989
p-value: 0.0
Critical Values: {'1%': -3.4307165437992815, '5%': -2.861701998398123, '10%': -2.5668562275081928}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17889
Model:                ARIMA(30, 1, 1)   Log Likelihood              -53978.006
Date:                Sun, 24 Dec 2023   AIC                         108020.012
Time:                        21:13:57   BIC                         108269.353
Sample:                             0   HQIC                        108102.032
                              - 17889                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.7040      0.150     -4.695      0.000      -0.998      -0.410
ar.L2         -0.0208      0.139     -0.150      0.881      -0.293       0.252
ar.L3         -0.0442      0.034     -1.302      0.193      -0.111       0.022
ar.L4          0.0057      0.015      0.384      0.701      -0.024       0.035
ar.L5         -0.0678      0.006    -11.011      0.000      -0.080      -0.056
ar.L6          0.0079      0.012      0.644      0.520      -0.016       0.032
ar.L7         -0.0269      0.007     -4.008      0.000      -0.040      -0.014
ar.L8          0.0352      0.008      4.378      0.000       0.019       0.051
ar.L9         -0.0361      0.008     -4.615      0.000      -0.051      -0.021
ar.L10         0.0394      0.009      4.509      0.000       0.022       0.057
ar.L11        -0.0058      0.008     -0.733      0.464      -0.021       0.010
ar.L12        -0.0007      0.007     -0.103      0.918      -0.014       0.013
ar.L13        -0.0109      0.007     -1.662      0.097      -0.024       0.002
ar.L14        -0.0242      0.006     -3.896      0.000      -0.036      -0.012
ar.L15        -0.0283      0.007     -4.132      0.000      -0.042      -0.015
ar.L16        -0.1744      0.007    -25.157      0.000      -0.188      -0.161
ar.L17        -0.0344      0.028     -1.245      0.213      -0.089       0.020
ar.L18         0.2606      0.012     22.126      0.000       0.238       0.284
ar.L19         0.4876      0.037     13.240      0.000       0.415       0.560
ar.L20        -0.0635      0.082     -0.780      0.436      -0.223       0.096
ar.L21        -0.1861      0.010    -18.062      0.000      -0.206      -0.166
ar.L22        -0.0377      0.027     -1.414      0.157      -0.090       0.015
ar.L23        -0.0699      0.013     -5.396      0.000      -0.095      -0.044
ar.L24         0.0003      0.015      0.021      0.984      -0.028       0.029
ar.L25        -0.0571      0.007     -7.723      0.000      -0.072      -0.043
ar.L26         0.0200      0.012      1.702      0.089      -0.003       0.043
ar.L27        -0.0194      0.007     -2.680      0.007      -0.034      -0.005
ar.L28         0.0317      0.007      4.308      0.000       0.017       0.046
ar.L29        -0.0426      0.008     -5.199      0.000      -0.059      -0.027
ar.L30         0.0522      0.011      4.854      0.000       0.031       0.073
ma.L1         -0.2195      0.150     -1.464      0.143      -0.513       0.074
sigma2        24.1995      0.119    203.778      0.000      23.967      24.432
===================================================================================
Ljung-Box (L1) (Q):                   0.10   Jarque-Bera (JB):            100499.86
Prob(Q):                              0.75   Prob(JB):                         0.00
Heteroskedasticity (H):             181.41   Skew:                            -0.28
Prob(H) (two-sided):                  0.00   Kurtosis:                        14.60
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17889    830.993420
17890    831.416583
17891    816.415864
17892    836.667463
17893    815.888341
17894    839.912712
17895    814.744336
17896    837.258024
17897    815.209181
17898    835.084600
Name: predicted_mean, dtype: float64
       lower price  upper price
17889   821.351778   840.635062
17890   821.746796   841.086369
17891   804.591520   828.240208
17892   824.623002   848.711924
17893   802.523481   829.253201
17894   826.325402   853.500022
17895   800.031282   829.457390
17896   822.326924   852.189124
17897   799.205572   831.212789
17898   818.916276   851.252925

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 18.254694830973946
Weighted Mean Absolute Percentage Error (WMAPE): 1.9625575975106204
In [725]:
forecastplusyahoo('GUBRF', 30, 1, 3)

                            price short_name
timestamp                                   
2021-12-27 09:00:00+03:00   77.30      GUBRF
2021-12-27 10:00:00+03:00   81.00      GUBRF
2021-12-27 11:00:00+03:00   81.15      GUBRF
2021-12-27 12:00:00+03:00   82.35      GUBRF
2021-12-27 13:00:00+03:00   83.00      GUBRF
...                           ...        ...
2023-09-22 14:00:00+03:00  337.20      GUBRF
2023-09-22 15:00:00+03:00  337.90      GUBRF
2023-09-22 16:00:00+03:00  337.40      GUBRF
2023-09-22 17:00:00+03:00  339.70      GUBRF
2023-09-22 18:00:00+03:00  339.50      GUBRF

[4323 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for GUBRF.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                               price
2021-12-27 06:00:00+00:00  77.300000
2021-12-27 06:30:00+00:00  79.150002
2021-12-27 07:00:00+00:00  81.000000
2021-12-27 07:30:00+00:00  80.949997
2021-12-27 08:00:00+00:00  81.150000
2021-12-27 08:30:00+00:00  82.150002
2021-12-27 09:00:00+00:00  82.350000
2021-12-27 09:30:00+00:00  82.300003
2021-12-27 10:00:00+00:00  83.000000
2021-12-27 10:30:00+00:00  82.900002
2021-12-27 11:00:00+00:00  82.500000
2021-12-27 11:30:00+00:00  82.050003
2021-12-27 12:00:00+00:00  83.150000
2021-12-27 12:30:00+00:00  82.650002
2021-12-27 13:00:00+00:00  83.100000
2021-12-27 13:30:00+00:00  83.050003
2021-12-27 14:00:00+00:00  83.150000
2021-12-27 15:00:00+00:00  83.500000
2021-12-28 06:00:00+00:00  85.600000
2021-12-28 06:30:00+00:00  84.349998
2021-12-28 07:00:00+00:00  84.250000
2021-12-28 07:30:00+00:00  83.800003
2021-12-28 08:00:00+00:00  82.850000
2021-12-28 08:30:00+00:00  82.900002
2021-12-28 09:00:00+00:00  82.900000
2021-12-28 09:30:00+00:00  82.949997
2021-12-28 10:00:00+00:00  83.500000
2021-12-28 10:30:00+00:00  84.250000
2021-12-28 11:00:00+00:00  83.950000
2021-12-28 11:30:00+00:00  84.199997
2021-12-28 12:00:00+00:00  83.600000
2021-12-28 12:30:00+00:00  83.250000
2021-12-28 13:00:00+00:00  83.000000
2021-12-28 13:30:00+00:00  81.349998
2021-12-28 14:00:00+00:00  81.850000
2021-12-28 15:00:00+00:00  81.850000
2021-12-29 06:00:00+00:00  81.900000
2021-12-29 06:30:00+00:00  81.300003
2021-12-29 07:00:00+00:00  81.350000
2021-12-29 07:30:00+00:00  82.400002
2021-12-29 08:00:00+00:00  82.000000
2021-12-29 08:30:00+00:00  81.750000
2021-12-29 09:00:00+00:00  81.800000
2021-12-29 09:30:00+00:00  82.000000
2021-12-29 10:00:00+00:00  82.000000
2021-12-29 10:30:00+00:00  81.900002
2021-12-29 11:00:00+00:00  81.650000
2021-12-29 11:30:00+00:00  82.000000
2021-12-29 12:00:00+00:00  81.800000
2021-12-29 12:30:00+00:00  82.000000
                                price
2023-09-21 10:00:00+00:00  328.800000
2023-09-21 10:30:00+00:00  330.899994
2023-09-21 11:00:00+00:00  332.800000
2023-09-21 11:30:00+00:00  333.200012
2023-09-21 12:00:00+00:00  335.400000
2023-09-21 12:30:00+00:00  336.899994
2023-09-21 13:00:00+00:00  337.700000
2023-09-21 13:30:00+00:00  339.399994
2023-09-21 14:00:00+00:00  340.600000
2023-09-21 14:30:00+00:00  340.600006
2023-09-21 15:00:00+00:00  340.500000
2023-09-22 06:00:00+00:00  341.500000
2023-09-22 06:30:00+00:00  340.500000
2023-09-22 07:00:00+00:00  339.600000
2023-09-22 07:30:00+00:00  340.700012
2023-09-22 08:00:00+00:00  340.600000
2023-09-22 08:30:00+00:00  339.899994
2023-09-22 09:00:00+00:00  338.700000
2023-09-22 09:30:00+00:00  337.100006
2023-09-22 10:00:00+00:00  339.700000
2023-09-22 10:30:00+00:00  336.799988
2023-09-22 11:00:00+00:00  337.200000
2023-09-22 11:30:00+00:00  338.000000
2023-09-22 12:00:00+00:00  337.900000
2023-09-22 12:30:00+00:00  339.899994
2023-09-22 13:00:00+00:00  337.400000
2023-09-22 13:30:00+00:00  336.600006
2023-09-22 14:00:00+00:00  339.700000
2023-09-22 14:30:00+00:00  339.700012
2023-09-22 15:00:00+00:00  339.500000
Mean of the first 10 values: price    344.27
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  342.0
2023-09-25 10:00:00+03:00  342.7
2023-09-25 11:00:00+03:00  342.5
2023-09-25 12:00:00+03:00  342.4
2023-09-25 13:00:00+03:00  342.3
2023-09-25 14:00:00+03:00  340.7
2023-09-25 15:00:00+03:00  345.7
2023-09-25 16:00:00+03:00  347.9
2023-09-25 17:00:00+03:00  348.0
2023-09-25 18:00:00+03:00  348.5
                              price
2018-01-02 10:00:00+03:00 -0.010000
2018-01-02 11:00:00+03:00  0.030000
2018-01-02 12:00:00+03:00  0.030000
2018-01-02 13:00:00+03:00  0.010000
2018-01-02 14:00:00+03:00  0.030000
...                             ...
2023-09-22 13:00:00+00:00 -2.499994
2023-09-22 13:30:00+00:00 -0.799994
2023-09-22 14:00:00+00:00  3.099994
2023-09-22 14:30:00+00:00  0.000012
2023-09-22 15:00:00+00:00 -0.200012

[17879 rows x 1 columns]
ADF Statistic: -18.346514139057238
p-value: 2.2407933134886963e-30
Critical Values: {'1%': -3.430716605446685, '5%': -2.861702025642245, '10%': -2.566856242009818}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17880
Model:                ARIMA(30, 3, 1)   Log Likelihood              -28209.135
Date:                Sun, 24 Dec 2023   AIC                          56482.270
Time:                        21:35:02   BIC                          56731.590
Sample:                             0   HQIC                         56564.285
                              - 17880                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.9813      0.003   -316.841      0.000      -0.987      -0.975
ar.L2         -0.9403      0.004   -213.397      0.000      -0.949      -0.932
ar.L3         -0.9096      0.006   -155.419      0.000      -0.921      -0.898
ar.L4         -0.8915      0.007   -126.073      0.000      -0.905      -0.878
ar.L5         -0.8836      0.007   -119.057      0.000      -0.898      -0.869
ar.L6         -0.8683      0.008   -103.749      0.000      -0.885      -0.852
ar.L7         -0.8286      0.009    -89.396      0.000      -0.847      -0.810
ar.L8         -0.7895      0.010    -78.777      0.000      -0.809      -0.770
ar.L9         -0.7583      0.010    -73.325      0.000      -0.779      -0.738
ar.L10        -0.7503      0.011    -70.524      0.000      -0.771      -0.729
ar.L11        -0.7133      0.011    -63.259      0.000      -0.735      -0.691
ar.L12        -0.6719      0.011    -60.220      0.000      -0.694      -0.650
ar.L13        -0.6437      0.011    -57.305      0.000      -0.666      -0.622
ar.L14        -0.6028      0.011    -55.124      0.000      -0.624      -0.581
ar.L15        -0.5796      0.011    -53.992      0.000      -0.601      -0.559
ar.L16        -0.5621      0.010    -53.683      0.000      -0.583      -0.542
ar.L17        -0.5200      0.010    -51.591      0.000      -0.540      -0.500
ar.L18        -0.4333      0.011    -41.064      0.000      -0.454      -0.413
ar.L19        -0.3758      0.010    -36.270      0.000      -0.396      -0.355
ar.L20        -0.3707      0.010    -37.189      0.000      -0.390      -0.351
ar.L21        -0.3597      0.009    -38.104      0.000      -0.378      -0.341
ar.L22        -0.3250      0.009    -35.540      0.000      -0.343      -0.307
ar.L23        -0.2597      0.009    -29.713      0.000      -0.277      -0.243
ar.L24        -0.2187      0.008    -26.737      0.000      -0.235      -0.203
ar.L25        -0.1853      0.008    -24.328      0.000      -0.200      -0.170
ar.L26        -0.1359      0.007    -18.168      0.000      -0.151      -0.121
ar.L27        -0.1201      0.007    -17.081      0.000      -0.134      -0.106
ar.L28        -0.0886      0.006    -14.603      0.000      -0.100      -0.077
ar.L29        -0.0960      0.005    -18.251      0.000      -0.106      -0.086
ar.L30        -0.0484      0.004    -13.251      0.000      -0.056      -0.041
ma.L1         -1.0000      0.008   -125.650      0.000      -1.016      -0.984
sigma2         1.3730      0.010    142.562      0.000       1.354       1.392
===================================================================================
Ljung-Box (L1) (Q):                   0.03   Jarque-Bera (JB):           2054817.20
Prob(Q):                              0.86   Prob(JB):                         0.00
Heteroskedasticity (H):             466.66   Skew:                            -0.12
Prob(H) (two-sided):                  0.00   Kurtosis:                        55.52
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17880    339.877964
17881    340.166978
17882    340.508472
17883    340.843389
17884    340.991366
17885    341.172664
17886    341.267921
17887    341.425627
17888    341.553862
17889    341.530783
Name: predicted_mean, dtype: float64
       lower price  upper price
17880   337.581292   342.174635
17881   336.888317   343.445640
17882   336.424535   344.592408
17883   336.050235   345.636544
17884   335.558650   346.424082
17885   335.157064   347.188264
17886   334.704431   347.831411
17887   334.319789   348.531466
17888   333.908299   349.199425
17889   333.350842   349.710723

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 4.134342823553692
Weighted Mean Absolute Percentage Error (WMAPE): 0.9916082336386941
In [721]:
forecastplusyahoo('GARAN', 30, 1, 1)

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00  10.6645      GARAN
2021-12-27 10:00:00+03:00  10.7373      GARAN
2021-12-27 11:00:00+03:00  10.7282      GARAN
2021-12-27 12:00:00+03:00  10.7282      GARAN
2021-12-27 13:00:00+03:00  10.7191      GARAN
...                            ...        ...
2023-09-22 14:00:00+03:00  51.3000      GARAN
2023-09-22 15:00:00+03:00  51.2500      GARAN
2023-09-22 16:00:00+03:00  51.4500      GARAN
2023-09-22 17:00:00+03:00  50.8500      GARAN
2023-09-22 18:00:00+03:00  50.8000      GARAN

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for GARAN.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                             price
2021-12-27 06:00:00+00:00  10.6645
2021-12-27 06:30:00+00:00  11.7300
2021-12-27 07:00:00+00:00  10.7373
2021-12-27 07:30:00+00:00  11.7700
2021-12-27 08:00:00+00:00  10.7282
2021-12-27 08:30:00+00:00  11.7900
2021-12-27 09:00:00+00:00  10.7282
2021-12-27 09:30:00+00:00  11.7600
2021-12-27 10:00:00+00:00  10.7191
2021-12-27 10:30:00+00:00  11.7700
2021-12-27 11:00:00+00:00  10.6736
2021-12-27 11:30:00+00:00  11.7200
2021-12-27 12:00:00+00:00  10.6645
2021-12-27 12:30:00+00:00  11.7100
2021-12-27 13:00:00+00:00  10.6645
2021-12-27 13:30:00+00:00  11.7500
2021-12-27 14:00:00+00:00  10.6372
2021-12-27 15:00:00+00:00  10.6190
2021-12-28 06:00:00+00:00  10.6009
2021-12-28 06:30:00+00:00  11.5900
2021-12-28 07:00:00+00:00  10.5371
2021-12-28 07:30:00+00:00  11.5800
2021-12-28 08:00:00+00:00  10.5007
2021-12-28 08:30:00+00:00  11.4900
2021-12-28 09:00:00+00:00  10.4553
2021-12-28 09:30:00+00:00  11.4700
2021-12-28 10:00:00+00:00  10.4462
2021-12-28 10:30:00+00:00  11.4800
2021-12-28 11:00:00+00:00  10.4189
2021-12-28 11:30:00+00:00  11.4800
2021-12-28 12:00:00+00:00  10.4553
2021-12-28 12:30:00+00:00  11.4800
2021-12-28 13:00:00+00:00  10.4280
2021-12-28 13:30:00+00:00  11.2300
2021-12-28 14:00:00+00:00  10.2278
2021-12-28 15:00:00+00:00  10.2278
2021-12-29 06:00:00+00:00  10.1913
2021-12-29 06:30:00+00:00  11.2300
2021-12-29 07:00:00+00:00  10.2642
2021-12-29 07:30:00+00:00  11.4000
2021-12-29 08:00:00+00:00  10.4371
2021-12-29 08:30:00+00:00  11.4700
2021-12-29 09:00:00+00:00  10.4280
2021-12-29 09:30:00+00:00  11.4500
2021-12-29 10:00:00+00:00  10.4553
2021-12-29 10:30:00+00:00  11.4500
2021-12-29 11:00:00+00:00  10.4189
2021-12-29 11:30:00+00:00  11.4600
2021-12-29 12:00:00+00:00  10.4098
2021-12-29 12:30:00+00:00  11.4700
                               price
2023-09-21 10:00:00+00:00  52.900000
2023-09-21 10:30:00+00:00  50.849998
2023-09-21 11:00:00+00:00  51.200000
2023-09-21 11:30:00+00:00  50.950001
2023-09-21 12:00:00+00:00  51.150000
2023-09-21 12:30:00+00:00  51.750000
2023-09-21 13:00:00+00:00  51.900000
2023-09-21 13:30:00+00:00  51.900002
2023-09-21 14:00:00+00:00  51.850000
2023-09-21 14:30:00+00:00  51.849998
2023-09-21 15:00:00+00:00  52.000000
2023-09-22 06:00:00+00:00  52.000000
2023-09-22 06:30:00+00:00  51.750000
2023-09-22 07:00:00+00:00  51.600000
2023-09-22 07:30:00+00:00  51.900002
2023-09-22 08:00:00+00:00  51.750000
2023-09-22 08:30:00+00:00  51.650002
2023-09-22 09:00:00+00:00  51.750000
2023-09-22 09:30:00+00:00  51.849998
2023-09-22 10:00:00+00:00  52.050000
2023-09-22 10:30:00+00:00  51.700001
2023-09-22 11:00:00+00:00  51.300000
2023-09-22 11:30:00+00:00  51.299999
2023-09-22 12:00:00+00:00  51.250000
2023-09-22 12:30:00+00:00  51.799999
2023-09-22 13:00:00+00:00  51.450000
2023-09-22 13:30:00+00:00  51.299999
2023-09-22 14:00:00+00:00  50.850000
2023-09-22 14:30:00+00:00  50.849998
2023-09-22 15:00:00+00:00  50.800000
Mean of the first 10 values: price    51.44
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  50.95
2023-09-25 10:00:00+03:00  51.05
2023-09-25 11:00:00+03:00  51.20
2023-09-25 12:00:00+03:00  51.35
2023-09-25 13:00:00+03:00  51.85
2023-09-25 14:00:00+03:00  51.40
2023-09-25 15:00:00+03:00  51.40
2023-09-25 16:00:00+03:00  52.05
2023-09-25 17:00:00+03:00  51.60
2023-09-25 18:00:00+03:00  51.55
                              price
2018-01-02 10:00:00+03:00  0.111000
2018-01-02 11:00:00+03:00  0.025700
2018-01-02 12:00:00+03:00 -0.017200
2018-01-02 13:00:00+03:00  0.008600
2018-01-02 14:00:00+03:00  0.008600
...                             ...
2023-09-22 13:00:00+00:00 -0.349999
2023-09-22 13:30:00+00:00 -0.150001
2023-09-22 14:00:00+00:00 -0.449999
2023-09-22 14:30:00+00:00 -0.000002
2023-09-22 15:00:00+00:00 -0.049998

[17890 rows x 1 columns]
ADF Statistic: -19.401740717527787
p-value: 0.0
Critical Values: {'1%': -3.4307165027125293, '5%': -2.861701980240464, '10%': -2.5668562178431515}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17891
Model:                ARIMA(30, 1, 1)   Log Likelihood               -3854.045
Date:                Sun, 24 Dec 2023   AIC                           7772.090
Time:                        21:19:02   BIC                           8021.434
Sample:                             0   HQIC                          7854.110
                              - 17891                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.8538      0.096     -8.924      0.000      -1.041      -0.666
ar.L2         -0.1218      0.071     -1.706      0.088      -0.262       0.018
ar.L3         -0.0691      0.007    -10.623      0.000      -0.082      -0.056
ar.L4         -0.0008      0.009     -0.093      0.926      -0.018       0.016
ar.L5         -0.0241      0.006     -3.862      0.000      -0.036      -0.012
ar.L6          0.0356      0.007      5.251      0.000       0.022       0.049
ar.L7         -0.0196      0.008     -2.553      0.011      -0.035      -0.005
ar.L8          0.0143      0.008      1.822      0.068      -0.001       0.030
ar.L9         -0.0220      0.008     -2.904      0.004      -0.037      -0.007
ar.L10         0.0278      0.008      3.518      0.000       0.012       0.043
ar.L11        -0.0072      0.007     -0.991      0.322      -0.021       0.007
ar.L12        -0.0195      0.007     -2.997      0.003      -0.032      -0.007
ar.L13        -0.0149      0.006     -2.352      0.019      -0.027      -0.002
ar.L14        -0.0283      0.006     -4.436      0.000      -0.041      -0.016
ar.L15        -0.0006      0.006     -0.097      0.923      -0.013       0.012
ar.L16        -0.1524      0.005    -30.835      0.000      -0.162      -0.143
ar.L17        -0.0943      0.015     -6.195      0.000      -0.124      -0.064
ar.L18         0.2712      0.008     32.134      0.000       0.255       0.288
ar.L19         0.4504      0.027     16.777      0.000       0.398       0.503
ar.L20        -0.0252      0.040     -0.625      0.532      -0.104       0.054
ar.L21        -0.1218      0.008    -14.708      0.000      -0.138      -0.106
ar.L22        -0.0250      0.012     -2.015      0.044      -0.049      -0.001
ar.L23        -0.0732      0.006    -11.893      0.000      -0.085      -0.061
ar.L24        -0.0224      0.009     -2.359      0.018      -0.041      -0.004
ar.L25        -0.0444      0.007     -6.256      0.000      -0.058      -0.030
ar.L26         0.0134      0.008      1.665      0.096      -0.002       0.029
ar.L27        -0.0295      0.007     -4.017      0.000      -0.044      -0.015
ar.L28         0.0108      0.009      1.256      0.209      -0.006       0.028
ar.L29        -0.0237      0.007     -3.455      0.001      -0.037      -0.010
ar.L30         0.0558      0.008      6.873      0.000       0.040       0.072
ma.L1          0.1107      0.096      1.156      0.248      -0.077       0.298
sigma2         0.0901      0.000    248.443      0.000       0.089       0.091
===================================================================================
Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):            259823.74
Prob(Q):                              0.94   Prob(JB):                         0.00
Heteroskedasticity (H):              34.11   Skew:                            -0.33
Prob(H) (two-sided):                  0.00   Kurtosis:                        21.66
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891    50.837913
17892    50.471711
17893    50.867086
17894    50.644726
17895    50.750837
17896    50.609562
17897    50.888290
17898    50.820875
17899    50.905305
17900    50.538152
Name: predicted_mean, dtype: float64
       lower price  upper price
17891    50.249661    51.426165
17892    49.864355    51.079067
17893    50.109596    51.624576
17894    49.859239    51.430212
17895    49.864852    51.636822
17896    49.693565    51.525559
17897    49.885751    51.890830
17898    49.791933    51.849817
17899    49.799110    52.011500
17900    49.408720    51.667583

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.7789229266620964
Weighted Mean Absolute Percentage Error (WMAPE): 1.3735503100311612
In [726]:
forecastplusyahoo('KRDMD', 30, 1, 3)

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00   8.8236      KRDMD
2021-12-27 10:00:00+03:00   9.1310      KRDMD
2021-12-27 11:00:00+03:00   9.0565      KRDMD
2021-12-27 12:00:00+03:00   9.0472      KRDMD
2021-12-27 13:00:00+03:00   9.0006      KRDMD
...                            ...        ...
2023-09-22 14:00:00+03:00  27.3200      KRDMD
2023-09-22 15:00:00+03:00  27.4000      KRDMD
2023-09-22 16:00:00+03:00  27.5600      KRDMD
2023-09-22 17:00:00+03:00  27.6200      KRDMD
2023-09-22 18:00:00+03:00  27.5200      KRDMD

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for KRDMD.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                            price
2021-12-27 06:00:00+00:00  8.8236
2021-12-27 06:30:00+00:00  9.6900
2021-12-27 07:00:00+00:00  9.1310
2021-12-27 07:30:00+00:00  9.7100
2021-12-27 08:00:00+00:00  9.0565
2021-12-27 08:30:00+00:00  9.7100
2021-12-27 09:00:00+00:00  9.0472
2021-12-27 09:30:00+00:00  9.6100
2021-12-27 10:00:00+00:00  9.0006
2021-12-27 10:30:00+00:00  9.6800
2021-12-27 11:00:00+00:00  8.9075
2021-12-27 11:30:00+00:00  9.4500
2021-12-27 12:00:00+00:00  8.8329
2021-12-27 12:30:00+00:00  9.4700
2021-12-27 13:00:00+00:00  8.8329
2021-12-27 13:30:00+00:00  9.5200
2021-12-27 14:00:00+00:00  8.7489
2021-12-27 15:00:00+00:00  8.7397
2021-12-28 06:00:00+00:00  8.8702
2021-12-28 06:30:00+00:00  9.4700
2021-12-28 07:00:00+00:00  8.8049
2021-12-28 07:30:00+00:00  9.4600
2021-12-28 08:00:00+00:00  8.7770
2021-12-28 08:30:00+00:00  9.3700
2021-12-28 09:00:00+00:00  8.7584
2021-12-28 09:30:00+00:00  9.3900
2021-12-28 10:00:00+00:00  8.7304
2021-12-28 10:30:00+00:00  9.3500
2021-12-28 11:00:00+00:00  8.5813
2021-12-28 11:30:00+00:00  9.2700
2021-12-28 12:00:00+00:00  8.6373
2021-12-28 12:30:00+00:00  9.2300
2021-12-28 13:00:00+00:00  8.5627
2021-12-28 13:30:00+00:00  9.0500
2021-12-28 14:00:00+00:00  8.3857
2021-12-28 15:00:00+00:00  8.3764
2021-12-29 06:00:00+00:00  8.3671
2021-12-29 06:30:00+00:00  8.8800
2021-12-29 07:00:00+00:00  8.3483
2021-12-29 07:30:00+00:00  9.1200
2021-12-29 08:00:00+00:00  8.5721
2021-12-29 08:30:00+00:00  9.1800
2021-12-29 09:00:00+00:00  8.5999
2021-12-29 09:30:00+00:00  9.2700
2021-12-29 10:00:00+00:00  8.6559
2021-12-29 10:30:00+00:00  9.2300
2021-12-29 11:00:00+00:00  8.6373
2021-12-29 11:30:00+00:00  9.2800
2021-12-29 12:00:00+00:00  8.6186
2021-12-29 12:30:00+00:00  9.3600
                               price
2023-09-21 10:00:00+00:00  25.580000
2023-09-21 10:30:00+00:00  25.900000
2023-09-21 11:00:00+00:00  26.060000
2023-09-21 11:30:00+00:00  26.040001
2023-09-21 12:00:00+00:00  25.960000
2023-09-21 12:30:00+00:00  26.200001
2023-09-21 13:00:00+00:00  26.220000
2023-09-21 13:30:00+00:00  26.219999
2023-09-21 14:00:00+00:00  26.440000
2023-09-21 14:30:00+00:00  26.440001
2023-09-21 15:00:00+00:00  26.460000
2023-09-22 06:00:00+00:00  26.400000
2023-09-22 06:30:00+00:00  26.639999
2023-09-22 07:00:00+00:00  26.600000
2023-09-22 07:30:00+00:00  26.660000
2023-09-22 08:00:00+00:00  26.780000
2023-09-22 08:30:00+00:00  26.740000
2023-09-22 09:00:00+00:00  26.540000
2023-09-22 09:30:00+00:00  26.440001
2023-09-22 10:00:00+00:00  26.500000
2023-09-22 10:30:00+00:00  27.340000
2023-09-22 11:00:00+00:00  27.320000
2023-09-22 11:30:00+00:00  27.280001
2023-09-22 12:00:00+00:00  27.400000
2023-09-22 12:30:00+00:00  27.559999
2023-09-22 13:00:00+00:00  27.560000
2023-09-22 13:30:00+00:00  27.340000
2023-09-22 14:00:00+00:00  27.620000
2023-09-22 14:30:00+00:00  27.620001
2023-09-22 15:00:00+00:00  27.520000
Mean of the first 10 values: price    29.704
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  27.64
2023-09-25 10:00:00+03:00  28.60
2023-09-25 11:00:00+03:00  29.48
2023-09-25 12:00:00+03:00  29.76
2023-09-25 13:00:00+03:00  30.26
2023-09-25 14:00:00+03:00  30.26
2023-09-25 15:00:00+03:00  30.26
2023-09-25 16:00:00+03:00  30.26
2023-09-25 17:00:00+03:00  30.26
2023-09-25 18:00:00+03:00  30.26
                                  price
2018-01-02 10:00:00+03:00 -1.660000e-02
2018-01-02 11:00:00+03:00  4.150000e-02
2018-01-02 12:00:00+03:00 -2.490000e-02
2018-01-02 13:00:00+03:00  0.000000e+00
2018-01-02 14:00:00+03:00  8.300000e-03
...                                 ...
2023-09-22 13:00:00+00:00  5.340576e-07
2023-09-22 13:30:00+00:00 -2.199998e-01
2023-09-22 14:00:00+00:00  2.799998e-01
2023-09-22 14:30:00+00:00  8.392334e-07
2023-09-22 15:00:00+00:00 -1.000008e-01

[17890 rows x 1 columns]
ADF Statistic: -19.553223976995625
p-value: 0.0
Critical Values: {'1%': -3.4307165027125293, '5%': -2.861701980240464, '10%': -2.5668562178431515}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17891
Model:                ARIMA(30, 3, 1)   Log Likelihood                8567.696
Date:                Sun, 24 Dec 2023   AIC                         -17071.393
Time:                        21:38:09   BIC                         -16822.052
Sample:                             0   HQIC                        -16989.373
                              - 17891                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.4345      0.004   -319.177      0.000      -1.443      -1.426
ar.L2         -1.2996      0.010   -134.545      0.000      -1.318      -1.281
ar.L3         -1.3753      0.014    -99.202      0.000      -1.403      -1.348
ar.L4         -1.2968      0.018    -73.375      0.000      -1.331      -1.262
ar.L5         -1.3124      0.021    -62.971      0.000      -1.353      -1.272
ar.L6         -1.2405      0.024    -52.041      0.000      -1.287      -1.194
ar.L7         -1.1949      0.026    -45.585      0.000      -1.246      -1.144
ar.L8         -1.1233      0.028    -40.800      0.000      -1.177      -1.069
ar.L9         -1.0947      0.028    -38.523      0.000      -1.150      -1.039
ar.L10        -1.0458      0.030    -35.253      0.000      -1.104      -0.988
ar.L11        -1.0208      0.031    -33.275      0.000      -1.081      -0.961
ar.L12        -1.0066      0.031    -32.069      0.000      -1.068      -0.945
ar.L13        -0.9101      0.032    -28.630      0.000      -0.972      -0.848
ar.L14        -0.8829      0.032    -27.691      0.000      -0.945      -0.820
ar.L15        -0.8420      0.032    -26.350      0.000      -0.905      -0.779
ar.L16        -0.9008      0.031    -28.728      0.000      -0.962      -0.839
ar.L17        -0.9473      0.031    -30.827      0.000      -1.008      -0.887
ar.L18        -0.5339      0.030    -17.505      0.000      -0.594      -0.474
ar.L19        -0.3136      0.029    -10.796      0.000      -0.371      -0.257
ar.L20        -0.4760      0.027    -17.436      0.000      -0.530      -0.423
ar.L21        -0.4426      0.026    -16.936      0.000      -0.494      -0.391
ar.L22        -0.4320      0.025    -17.436      0.000      -0.481      -0.383
ar.L23        -0.3833      0.024    -16.168      0.000      -0.430      -0.337
ar.L24        -0.3191      0.022    -14.785      0.000      -0.361      -0.277
ar.L25        -0.2899      0.020    -14.618      0.000      -0.329      -0.251
ar.L26        -0.2068      0.018    -11.410      0.000      -0.242      -0.171
ar.L27        -0.1695      0.016    -10.326      0.000      -0.202      -0.137
ar.L28        -0.0790      0.014     -5.801      0.000      -0.106      -0.052
ar.L29        -0.0388      0.010     -3.746      0.000      -0.059      -0.018
ar.L30         0.0619      0.006      9.986      0.000       0.050       0.074
ma.L1         -0.9498      0.003   -291.510      0.000      -0.956      -0.943
sigma2         0.0224   9.22e-05    243.497      0.000       0.022       0.023
===================================================================================
Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):            330911.07
Prob(Q):                              0.91   Prob(JB):                         0.00
Heteroskedasticity (H):              48.43   Skew:                             0.24
Prob(H) (two-sided):                  0.00   Kurtosis:                        24.07
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891    27.721452
17892    27.777728
17893    27.796666
17894    28.000454
17895    28.020671
17896    28.044355
17897    27.959670
17898    28.077803
17899    28.488024
17900    28.519237
Name: predicted_mean, dtype: float64
       lower price  upper price
17891    27.427813    28.015091
17892    27.432896    28.122559
17893    27.349821    28.243512
17894    27.503916    28.496992
17895    27.437305    28.604038
17896    27.409055    28.679656
17897    27.239923    28.679418
17898    27.298213    28.857393
17899    27.621478    29.354571
17900    27.587817    29.450657

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 1.8099643456148864
Weighted Mean Absolute Percentage Error (WMAPE): 5.869630706095556
In [727]:
forecastplusyahoo('KCHOL',30,1,3)

                              price short_name
timestamp                                     
2021-12-27 09:00:00+03:00   28.3315      KCHOL
2021-12-27 10:00:00+03:00   28.7712      KCHOL
2021-12-27 11:00:00+03:00   28.6565      KCHOL
2021-12-27 12:00:00+03:00   28.6375      KCHOL
2021-12-27 13:00:00+03:00   28.6565      KCHOL
...                             ...        ...
2023-09-22 14:00:00+03:00  138.8000      KCHOL
2023-09-22 15:00:00+03:00  138.9000      KCHOL
2023-09-22 16:00:00+03:00  139.0000      KCHOL
2023-09-22 17:00:00+03:00  137.3000      KCHOL
2023-09-22 18:00:00+03:00  137.5000      KCHOL

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for KCHOL.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                               price
2021-12-27 06:00:00+00:00  28.331500
2021-12-27 06:30:00+00:00  30.040001
2021-12-27 07:00:00+00:00  28.771200
2021-12-27 07:30:00+00:00  30.100000
2021-12-27 08:00:00+00:00  28.656500
2021-12-27 08:30:00+00:00  30.020000
2021-12-27 09:00:00+00:00  28.637500
2021-12-27 09:30:00+00:00  29.860001
2021-12-27 10:00:00+00:00  28.656500
2021-12-27 10:30:00+00:00  29.719999
2021-12-27 11:00:00+00:00  28.236000
2021-12-27 11:30:00+00:00  29.639999
2021-12-27 12:00:00+00:00  28.427200
2021-12-27 12:30:00+00:00  29.700001
2021-12-27 13:00:00+00:00  28.465400
2021-12-27 13:30:00+00:00  29.820000
2021-12-27 14:00:00+00:00  28.197700
2021-12-27 15:00:00+00:00  28.140400
2021-12-28 06:00:00+00:00  28.255000
2021-12-28 06:30:00+00:00  29.240000
2021-12-28 07:00:00+00:00  28.063900
2021-12-28 07:30:00+00:00  29.139999
2021-12-28 08:00:00+00:00  27.738900
2021-12-28 08:30:00+00:00  29.000000
2021-12-28 09:00:00+00:00  27.681500
2021-12-28 09:30:00+00:00  28.860001
2021-12-28 10:00:00+00:00  27.719800
2021-12-28 10:30:00+00:00  28.959999
2021-12-28 11:00:00+00:00  27.547700
2021-12-28 11:30:00+00:00  28.940001
2021-12-28 12:00:00+00:00  27.643300
2021-12-28 12:30:00+00:00  28.840000
2021-12-28 13:00:00+00:00  27.375700
2021-12-28 13:30:00+00:00  28.100000
2021-12-28 14:00:00+00:00  27.184500
2021-12-28 15:00:00+00:00  27.433000
2021-12-29 06:00:00+00:00  27.318400
2021-12-29 06:30:00+00:00  28.160000
2021-12-29 07:00:00+00:00  27.031600
2021-12-29 07:30:00+00:00  28.760000
2021-12-29 08:00:00+00:00  27.605100
2021-12-29 08:30:00+00:00  28.840000
2021-12-29 09:00:00+00:00  27.719800
2021-12-29 09:30:00+00:00  28.940001
2021-12-29 10:00:00+00:00  27.719800
2021-12-29 10:30:00+00:00  28.860001
2021-12-29 11:00:00+00:00  27.777200
2021-12-29 11:30:00+00:00  29.320000
2021-12-29 12:00:00+00:00  28.025700
2021-12-29 12:30:00+00:00  29.600000
                                price
2023-09-21 10:00:00+00:00  134.500000
2023-09-21 10:30:00+00:00  134.500000
2023-09-21 11:00:00+00:00  136.900000
2023-09-21 11:30:00+00:00  137.399994
2023-09-21 12:00:00+00:00  137.000000
2023-09-21 12:30:00+00:00  138.500000
2023-09-21 13:00:00+00:00  136.900000
2023-09-21 13:30:00+00:00  138.199997
2023-09-21 14:00:00+00:00  138.700000
2023-09-21 14:30:00+00:00  138.699997
2023-09-21 15:00:00+00:00  138.800000
2023-09-22 06:00:00+00:00  139.300000
2023-09-22 06:30:00+00:00  140.199997
2023-09-22 07:00:00+00:00  140.600000
2023-09-22 07:30:00+00:00  140.199997
2023-09-22 08:00:00+00:00  139.300000
2023-09-22 08:30:00+00:00  139.800003
2023-09-22 09:00:00+00:00  140.000000
2023-09-22 09:30:00+00:00  139.800003
2023-09-22 10:00:00+00:00  139.800000
2023-09-22 10:30:00+00:00  139.199997
2023-09-22 11:00:00+00:00  138.800000
2023-09-22 11:30:00+00:00  138.500000
2023-09-22 12:00:00+00:00  138.900000
2023-09-22 12:30:00+00:00  139.100006
2023-09-22 13:00:00+00:00  139.000000
2023-09-22 13:30:00+00:00  137.800003
2023-09-22 14:00:00+00:00  137.300000
2023-09-22 14:30:00+00:00  137.399994
2023-09-22 15:00:00+00:00  137.500000
Mean of the first 10 values: price    141.23
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  138.0
2023-09-25 10:00:00+03:00  139.8
2023-09-25 11:00:00+03:00  139.9
2023-09-25 12:00:00+03:00  141.2
2023-09-25 13:00:00+03:00  141.9
2023-09-25 14:00:00+03:00  142.2
2023-09-25 15:00:00+03:00  142.2
2023-09-25 16:00:00+03:00  142.8
2023-09-25 17:00:00+03:00  142.3
2023-09-25 18:00:00+03:00  142.0
                              price
2018-01-02 10:00:00+03:00 -0.026200
2018-01-02 11:00:00+03:00  0.252400
2018-01-02 12:00:00+03:00 -0.087000
2018-01-02 13:00:00+03:00  0.078200
2018-01-02 14:00:00+03:00  0.130500
...                             ...
2023-09-22 13:00:00+00:00 -0.100006
2023-09-22 13:30:00+00:00 -1.199997
2023-09-22 14:00:00+00:00 -0.500003
2023-09-22 14:30:00+00:00  0.099994
2023-09-22 15:00:00+00:00  0.100006

[17889 rows x 1 columns]
ADF Statistic: -20.86062436034284
p-value: 0.0
Critical Values: {'1%': -3.4307165232547536, '5%': -2.8617019893187847, '10%': -2.5668562226754013}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17890
Model:                ARIMA(30, 3, 1)   Log Likelihood              -11538.687
Date:                Sun, 24 Dec 2023   AIC                          23141.374
Time:                        21:42:00   BIC                          23390.713
Sample:                             0   HQIC                         23223.393
                              - 17890                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.3188      0.003   -420.000      0.000      -1.325      -1.313
ar.L2         -1.1751      0.005   -225.618      0.000      -1.185      -1.165
ar.L3         -1.2516      0.007   -176.232      0.000      -1.266      -1.238
ar.L4         -1.1343      0.009   -131.957      0.000      -1.151      -1.117
ar.L5         -1.1306      0.010   -113.863      0.000      -1.150      -1.111
ar.L6         -1.0223      0.011    -90.294      0.000      -1.045      -1.000
ar.L7         -1.0085      0.013    -80.556      0.000      -1.033      -0.984
ar.L8         -0.9694      0.013    -74.273      0.000      -0.995      -0.944
ar.L9         -0.9603      0.013    -71.867      0.000      -0.986      -0.934
ar.L10        -0.8597      0.014    -61.986      0.000      -0.887      -0.832
ar.L11        -0.7909      0.014    -56.440      0.000      -0.818      -0.763
ar.L12        -0.7569      0.014    -53.778      0.000      -0.784      -0.729
ar.L13        -0.7003      0.014    -49.492      0.000      -0.728      -0.673
ar.L14        -0.6733      0.013    -50.081      0.000      -0.700      -0.647
ar.L15        -0.6040      0.013    -45.932      0.000      -0.630      -0.578
ar.L16        -0.6054      0.013    -46.286      0.000      -0.631      -0.580
ar.L17        -0.6090      0.012    -49.128      0.000      -0.633      -0.585
ar.L18        -0.4062      0.012    -33.790      0.000      -0.430      -0.383
ar.L19        -0.3028      0.012    -24.884      0.000      -0.327      -0.279
ar.L20        -0.3849      0.012    -31.584      0.000      -0.409      -0.361
ar.L21        -0.3496      0.012    -29.115      0.000      -0.373      -0.326
ar.L22        -0.3328      0.012    -28.399      0.000      -0.356      -0.310
ar.L23        -0.2916      0.011    -25.451      0.000      -0.314      -0.269
ar.L24        -0.2413      0.011    -22.671      0.000      -0.262      -0.220
ar.L25        -0.2025      0.010    -21.150      0.000      -0.221      -0.184
ar.L26        -0.0917      0.009    -10.668      0.000      -0.109      -0.075
ar.L27        -0.0672      0.008     -8.340      0.000      -0.083      -0.051
ar.L28        -0.0145      0.007     -1.990      0.047      -0.029      -0.000
ar.L29        -0.0410      0.007     -5.958      0.000      -0.054      -0.028
ar.L30         0.0388      0.005      8.365      0.000       0.030       0.048
ma.L1         -0.9997      0.001   -734.949      0.000      -1.002      -0.997
sigma2         0.2116      0.001    285.044      0.000       0.210       0.213
===================================================================================
Ljung-Box (L1) (Q):                   0.16   Jarque-Bera (JB):            580107.41
Prob(Q):                              0.69   Prob(JB):                         0.00
Heteroskedasticity (H):              45.60   Skew:                            -0.15
Prob(H) (two-sided):                  0.00   Kurtosis:                        30.90
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890    137.709208
17891    137.788827
17892    137.606286
17893    137.502931
17894    137.490099
17895    137.916505
17896    137.454016
17897    137.748910
17898    137.483078
17899    137.513709
Name: predicted_mean, dtype: float64
       lower price  upper price
17890   136.807730   138.610687
17891   136.697914   138.879740
17892   136.232038   138.980535
17893   135.980475   139.025387
17894   135.735288   139.244910
17895   136.015791   139.817219
17896   135.331969   139.576063
17897   135.481144   140.016676
17898   135.017534   139.948623
17899   134.908361   140.119057

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 3.9015612302972906
Weighted Mean Absolute Percentage Error (WMAPE): 2.5551533958717365
In [728]:
forecastplusyahoo('KOZAL', 30, 1, 1)

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00   4.9696      KOZAL
2021-12-27 10:00:00+03:00   5.0971      KOZAL
2021-12-27 11:00:00+03:00   5.0716      KOZAL
2021-12-27 12:00:00+03:00   5.0504      KOZAL
2021-12-27 13:00:00+03:00   5.0716      KOZAL
...                            ...        ...
2023-09-22 14:00:00+03:00  28.1200      KOZAL
2023-09-22 15:00:00+03:00  28.0400      KOZAL
2023-09-22 16:00:00+03:00  28.0400      KOZAL
2023-09-22 17:00:00+03:00  27.9400      KOZAL
2023-09-22 18:00:00+03:00  28.0200      KOZAL

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for KOZAL.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                              price
2021-12-27 06:00:00+00:00  4.969600
2021-12-27 06:30:00+00:00  0.267347
2021-12-27 07:00:00+00:00  5.097100
2021-12-27 07:30:00+00:00  0.270748
2021-12-27 08:00:00+00:00  5.071600
2021-12-27 08:30:00+00:00  0.270748
2021-12-27 09:00:00+00:00  5.050400
2021-12-27 09:30:00+00:00  0.268254
2021-12-27 10:00:00+00:00  5.071600
2021-12-27 10:30:00+00:00  0.271202
2021-12-27 11:00:00+00:00  5.033400
2021-12-27 11:30:00+00:00  0.267800
2021-12-27 12:00:00+00:00  5.033400
2021-12-27 12:30:00+00:00  0.269388
2021-12-27 13:00:00+00:00  5.050400
2021-12-27 13:30:00+00:00  0.268481
2021-12-27 14:00:00+00:00  4.995200
2021-12-27 15:00:00+00:00  5.012100
2021-12-28 06:00:00+00:00  5.080200
2021-12-28 06:30:00+00:00  0.273696
2021-12-28 07:00:00+00:00  5.143900
2021-12-28 07:30:00+00:00  0.273016
2021-12-28 08:00:00+00:00  5.088700
2021-12-28 08:30:00+00:00  0.271429
2021-12-28 09:00:00+00:00  5.088700
2021-12-28 09:30:00+00:00  0.271655
2021-12-28 10:00:00+00:00  5.097100
2021-12-28 10:30:00+00:00  0.270748
2021-12-28 11:00:00+00:00  5.054700
2021-12-28 11:30:00+00:00  0.273016
2021-12-28 12:00:00+00:00  5.135500
2021-12-28 12:30:00+00:00  0.274150
2021-12-28 13:00:00+00:00  5.105700
2021-12-28 13:30:00+00:00  0.267574
2021-12-28 14:00:00+00:00  5.071600
2021-12-28 15:00:00+00:00  5.084400
2021-12-29 06:00:00+00:00  5.084400
2021-12-29 06:30:00+00:00  0.266667
2021-12-29 07:00:00+00:00  4.999400
2021-12-29 07:30:00+00:00  0.268027
2021-12-29 08:00:00+00:00  5.020700
2021-12-29 08:30:00+00:00  0.266893
2021-12-29 09:00:00+00:00  5.024900
2021-12-29 09:30:00+00:00  0.267574
2021-12-29 10:00:00+00:00  5.020700
2021-12-29 10:30:00+00:00  0.266667
2021-12-29 11:00:00+00:00  5.020700
2021-12-29 11:30:00+00:00  0.268027
2021-12-29 12:00:00+00:00  5.037600
2021-12-29 12:30:00+00:00  0.270068
                               price
2023-09-21 10:00:00+00:00  27.200000
2023-09-21 10:30:00+00:00  27.780001
2023-09-21 11:00:00+00:00  27.820000
2023-09-21 11:30:00+00:00  27.799999
2023-09-21 12:00:00+00:00  27.720000
2023-09-21 12:30:00+00:00  28.120001
2023-09-21 13:00:00+00:00  28.000000
2023-09-21 13:30:00+00:00  28.080000
2023-09-21 14:00:00+00:00  28.460000
2023-09-21 14:30:00+00:00  28.459999
2023-09-21 15:00:00+00:00  28.600000
2023-09-22 06:00:00+00:00  28.780000
2023-09-22 06:30:00+00:00  28.520000
2023-09-22 07:00:00+00:00  28.460000
2023-09-22 07:30:00+00:00  28.400000
2023-09-22 08:00:00+00:00  28.440000
2023-09-22 08:30:00+00:00  28.320000
2023-09-22 09:00:00+00:00  28.300000
2023-09-22 09:30:00+00:00  28.260000
2023-09-22 10:00:00+00:00  28.260000
2023-09-22 10:30:00+00:00  28.219999
2023-09-22 11:00:00+00:00  28.120000
2023-09-22 11:30:00+00:00  28.020000
2023-09-22 12:00:00+00:00  28.040000
2023-09-22 12:30:00+00:00  28.320000
2023-09-22 13:00:00+00:00  28.040000
2023-09-22 13:30:00+00:00  27.860001
2023-09-22 14:00:00+00:00  27.940000
2023-09-22 14:30:00+00:00  27.940001
2023-09-22 15:00:00+00:00  28.020000
Mean of the first 10 values: price    28.638
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  28.22
2023-09-25 10:00:00+03:00  28.38
2023-09-25 11:00:00+03:00  28.54
2023-09-25 12:00:00+03:00  28.58
2023-09-25 13:00:00+03:00  28.60
2023-09-25 14:00:00+03:00  28.68
2023-09-25 15:00:00+03:00  28.54
2023-09-25 16:00:00+03:00  28.88
2023-09-25 17:00:00+03:00  28.96
2023-09-25 18:00:00+03:00  29.00
                                  price
2018-01-02 10:00:00+03:00  2.040000e-02
2018-01-02 11:00:00+03:00 -5.100000e-03
2018-01-02 12:00:00+03:00 -3.400000e-03
2018-01-02 13:00:00+03:00  0.000000e+00
2018-01-02 14:00:00+03:00  1.440000e-02
...                                 ...
2023-09-22 13:00:00+00:00 -2.799997e-01
2023-09-22 13:30:00+00:00 -1.799994e-01
2023-09-22 14:00:00+00:00  7.999939e-02
2023-09-22 14:30:00+00:00  5.340576e-07
2023-09-22 15:00:00+00:00  7.999947e-02

[17889 rows x 1 columns]
ADF Statistic: -19.812067236570215
p-value: 0.0
Critical Values: {'1%': -3.4307165232547536, '5%': -2.8617019893187847, '10%': -2.5668562226754013}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17890
Model:                ARIMA(30, 1, 1)   Log Likelihood              -24459.787
Date:                Sun, 24 Dec 2023   AIC                          48983.574
Time:                        21:44:33   BIC                          49232.916
Sample:                             0   HQIC                         49065.594
                              - 17890                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.6103      0.033    -18.377      0.000      -0.675      -0.545
ar.L2          0.3376      0.049      6.853      0.000       0.241       0.434
ar.L3          0.1726      0.032      5.418      0.000       0.110       0.235
ar.L4          0.0346      0.021      1.642      0.101      -0.007       0.076
ar.L5         -0.0061      0.017     -0.359      0.720      -0.040       0.027
ar.L6          0.0416      0.016      2.560      0.010       0.010       0.073
ar.L7         -0.0090      0.012     -0.729      0.466      -0.033       0.015
ar.L8          0.0022      0.011      0.196      0.844      -0.020       0.024
ar.L9         -0.0221      0.011     -1.944      0.052      -0.044       0.000
ar.L10         0.0082      0.011      0.734      0.463      -0.014       0.030
ar.L11        -0.0317      0.008     -3.759      0.000      -0.048      -0.015
ar.L12        -0.1005      0.009    -11.439      0.000      -0.118      -0.083
ar.L13        -0.0544      0.011     -4.960      0.000      -0.076      -0.033
ar.L14        -0.0685      0.011     -6.371      0.000      -0.090      -0.047
ar.L15        -0.0189      0.011     -1.693      0.090      -0.041       0.003
ar.L16        -0.1105      0.011    -10.409      0.000      -0.131      -0.090
ar.L17         0.0134      0.013      1.056      0.291      -0.011       0.038
ar.L18         0.7772      0.010     75.208      0.000       0.757       0.797
ar.L19         0.5681      0.017     32.620      0.000       0.534       0.602
ar.L20        -0.3564      0.034    -10.429      0.000      -0.423      -0.289
ar.L21        -0.2714      0.018    -14.931      0.000      -0.307      -0.236
ar.L22        -0.0520      0.007     -7.525      0.000      -0.066      -0.038
ar.L23        -0.0649      0.005    -14.041      0.000      -0.074      -0.056
ar.L24        -0.0530      0.004    -12.750      0.000      -0.061      -0.045
ar.L25        -0.0637      0.004    -16.143      0.000      -0.071      -0.056
ar.L26        -0.0181      0.006     -2.992      0.003      -0.030      -0.006
ar.L27        -0.0485      0.007     -6.750      0.000      -0.063      -0.034
ar.L28        -0.0258      0.008     -3.371      0.001      -0.041      -0.011
ar.L29        -0.0452      0.008     -5.485      0.000      -0.061      -0.029
ar.L30         0.0725      0.009      8.421      0.000       0.056       0.089
ma.L1         -0.8643      0.034    -25.791      0.000      -0.930      -0.799
sigma2         0.9016      0.002    410.194      0.000       0.897       0.906
===================================================================================
Ljung-Box (L1) (Q):                   0.03   Jarque-Bera (JB):          26885429.13
Prob(Q):                              0.87   Prob(JB):                         0.00
Heteroskedasticity (H):             896.52   Skew:                             5.27
Prob(H) (two-sided):                  0.00   Kurtosis:                       192.63
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890    28.152962
17891    27.811522
17892    28.015828
17893    27.859128
17894    27.928055
17895    27.796296
17896    27.850601
17897    27.810418
17898    27.884050
17899    27.754271
Name: predicted_mean, dtype: float64
       lower price  upper price
17890    26.291935    30.013989
17891    25.751531    29.871512
17892    25.513935    30.517720
17893    25.288383    30.429873
17894    25.168078    30.688033
17895    25.013344    30.579247
17896    24.943880    30.757322
17897    24.889329    30.731507
17898    24.865659    30.902441
17899    24.724415    30.784126

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.817514809053018
Weighted Mean Absolute Percentage Error (WMAPE): 2.624788856189999
In [732]:
forecastplusyahoo('KOZAA', 30, 1, 3)

                           price short_name
timestamp                                  
2021-12-27 09:00:00+03:00  17.76      KOZAA
2021-12-27 10:00:00+03:00  18.25      KOZAA
2021-12-27 11:00:00+03:00  18.04      KOZAA
2021-12-27 12:00:00+03:00  17.92      KOZAA
2021-12-27 13:00:00+03:00  18.14      KOZAA
...                          ...        ...
2023-09-22 14:00:00+03:00  64.60      KOZAA
2023-09-22 15:00:00+03:00  64.60      KOZAA
2023-09-22 16:00:00+03:00  64.45      KOZAA
2023-09-22 17:00:00+03:00  64.15      KOZAA
2023-09-22 18:00:00+03:00  64.45      KOZAA

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for KOZAA.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                               price
2021-12-27 06:00:00+00:00  17.760000
2021-12-27 06:30:00+00:00  17.959999
2021-12-27 07:00:00+00:00  18.250000
2021-12-27 07:30:00+00:00  18.129999
2021-12-27 08:00:00+00:00  18.040000
2021-12-27 08:30:00+00:00  18.040001
2021-12-27 09:00:00+00:00  17.920000
2021-12-27 09:30:00+00:00  17.830000
2021-12-27 10:00:00+00:00  18.140000
2021-12-27 10:30:00+00:00  18.280001
2021-12-27 11:00:00+00:00  18.110000
2021-12-27 11:30:00+00:00  17.900000
2021-12-27 12:00:00+00:00  18.000000
2021-12-27 12:30:00+00:00  18.010000
2021-12-27 13:00:00+00:00  18.040000
2021-12-27 13:30:00+00:00  18.110001
2021-12-27 14:00:00+00:00  17.930000
2021-12-27 15:00:00+00:00  17.940000
2021-12-28 06:00:00+00:00  18.160000
2021-12-28 06:30:00+00:00  18.160000
2021-12-28 07:00:00+00:00  18.330000
2021-12-28 07:30:00+00:00  18.129999
2021-12-28 08:00:00+00:00  18.060000
2021-12-28 08:30:00+00:00  18.080000
2021-12-28 09:00:00+00:00  18.060000
2021-12-28 09:30:00+00:00  18.030001
2021-12-28 10:00:00+00:00  18.040000
2021-12-28 10:30:00+00:00  17.930000
2021-12-28 11:00:00+00:00  17.910000
2021-12-28 11:30:00+00:00  18.160000
2021-12-28 12:00:00+00:00  18.200000
2021-12-28 12:30:00+00:00  18.200001
2021-12-28 13:00:00+00:00  18.000000
2021-12-28 13:30:00+00:00  17.709999
2021-12-28 14:00:00+00:00  17.720000
2021-12-28 15:00:00+00:00  17.770000
2021-12-29 06:00:00+00:00  17.720000
2021-12-29 06:30:00+00:00  17.590000
2021-12-29 07:00:00+00:00  17.570000
2021-12-29 07:30:00+00:00  17.750000
2021-12-29 08:00:00+00:00  17.770000
2021-12-29 08:30:00+00:00  17.719999
2021-12-29 09:00:00+00:00  17.770000
2021-12-29 09:30:00+00:00  17.780001
2021-12-29 10:00:00+00:00  17.870000
2021-12-29 10:30:00+00:00  17.719999
2021-12-29 11:00:00+00:00  17.840000
2021-12-29 11:30:00+00:00  17.950001
2021-12-29 12:00:00+00:00  17.990000
2021-12-29 12:30:00+00:00  18.120001
                               price
2023-09-21 10:00:00+00:00  63.550000
2023-09-21 10:30:00+00:00  65.199997
2023-09-21 11:00:00+00:00  65.250000
2023-09-21 11:30:00+00:00  65.099998
2023-09-21 12:00:00+00:00  65.050000
2023-09-21 12:30:00+00:00  66.050003
2023-09-21 13:00:00+00:00  65.850000
2023-09-21 13:30:00+00:00  66.050003
2023-09-21 14:00:00+00:00  66.750000
2023-09-21 14:30:00+00:00  66.750000
2023-09-21 15:00:00+00:00  66.650000
2023-09-22 06:00:00+00:00  65.800000
2023-09-22 06:30:00+00:00  65.900002
2023-09-22 07:00:00+00:00  65.900000
2023-09-22 07:30:00+00:00  65.750000
2023-09-22 08:00:00+00:00  66.000000
2023-09-22 08:30:00+00:00  65.449997
2023-09-22 09:00:00+00:00  65.400000
2023-09-22 09:30:00+00:00  65.099998
2023-09-22 10:00:00+00:00  65.200000
2023-09-22 10:30:00+00:00  64.800003
2023-09-22 11:00:00+00:00  64.600000
2023-09-22 11:30:00+00:00  64.449997
2023-09-22 12:00:00+00:00  64.600000
2023-09-22 12:30:00+00:00  65.000000
2023-09-22 13:00:00+00:00  64.450000
2023-09-22 13:30:00+00:00  64.150002
2023-09-22 14:00:00+00:00  64.150000
2023-09-22 14:30:00+00:00  64.150002
2023-09-22 15:00:00+00:00  64.450000
Mean of the first 10 values: price    65.285
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  65.30
2023-09-25 10:00:00+03:00  65.15
2023-09-25 11:00:00+03:00  65.00
2023-09-25 12:00:00+03:00  65.20
2023-09-25 13:00:00+03:00  65.55
2023-09-25 14:00:00+03:00  65.60
2023-09-25 15:00:00+03:00  64.75
2023-09-25 16:00:00+03:00  65.25
2023-09-25 17:00:00+03:00  65.50
2023-09-25 18:00:00+03:00  65.55
                              price
2018-01-02 10:00:00+03:00  0.020000
2018-01-02 11:00:00+03:00 -0.030000
2018-01-02 12:00:00+03:00 -0.020000
2018-01-02 13:00:00+03:00  0.000000
2018-01-02 14:00:00+03:00  0.020000
...                             ...
2023-09-22 13:00:00+00:00 -0.550000
2023-09-22 13:30:00+00:00 -0.299998
2023-09-22 14:00:00+00:00 -0.000002
2023-09-22 14:30:00+00:00  0.000002
2023-09-22 15:00:00+00:00  0.299998

[17889 rows x 1 columns]
ADF Statistic: -19.13934986413561
p-value: 0.0
Critical Values: {'1%': -3.4307165232547536, '5%': -2.8617019893187847, '10%': -2.5668562226754013}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17890
Model:                ARIMA(30, 3, 1)   Log Likelihood               -1801.296
Date:                Sun, 24 Dec 2023   AIC                           3666.593
Time:                        21:55:42   BIC                           3915.931
Sample:                             0   HQIC                          3748.612
                              - 17890                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.9633      0.003   -309.784      0.000      -0.969      -0.957
ar.L2         -0.9478      0.004   -217.776      0.000      -0.956      -0.939
ar.L3         -0.9094      0.006   -148.910      0.000      -0.921      -0.897
ar.L4         -0.8850      0.007   -125.340      0.000      -0.899      -0.871
ar.L5         -0.8482      0.008   -101.416      0.000      -0.865      -0.832
ar.L6         -0.8448      0.010    -88.119      0.000      -0.864      -0.826
ar.L7         -0.8126      0.010    -80.371      0.000      -0.832      -0.793
ar.L8         -0.7759      0.011    -73.389      0.000      -0.797      -0.755
ar.L9         -0.7314      0.011    -66.506      0.000      -0.753      -0.710
ar.L10        -0.7053      0.012    -59.090      0.000      -0.729      -0.682
ar.L11        -0.6518      0.012    -52.993      0.000      -0.676      -0.628
ar.L12        -0.6209      0.013    -49.075      0.000      -0.646      -0.596
ar.L13        -0.5997      0.013    -46.854      0.000      -0.625      -0.575
ar.L14        -0.5304      0.013    -41.228      0.000      -0.556      -0.505
ar.L15        -0.4973      0.013    -38.919      0.000      -0.522      -0.472
ar.L16        -0.4701      0.013    -36.809      0.000      -0.495      -0.445
ar.L17        -0.4182      0.012    -33.562      0.000      -0.443      -0.394
ar.L18        -0.3792      0.012    -30.958      0.000      -0.403      -0.355
ar.L19        -0.3566      0.012    -29.992      0.000      -0.380      -0.333
ar.L20        -0.3590      0.011    -31.303      0.000      -0.381      -0.336
ar.L21        -0.3655      0.011    -31.950      0.000      -0.388      -0.343
ar.L22        -0.3543      0.011    -32.079      0.000      -0.376      -0.333
ar.L23        -0.3059      0.011    -27.864      0.000      -0.327      -0.284
ar.L24        -0.2709      0.010    -25.841      0.000      -0.291      -0.250
ar.L25        -0.2385      0.010    -24.341      0.000      -0.258      -0.219
ar.L26        -0.2035      0.009    -22.816      0.000      -0.221      -0.186
ar.L27        -0.1653      0.008    -21.126      0.000      -0.181      -0.150
ar.L28        -0.1119      0.007    -15.990      0.000      -0.126      -0.098
ar.L29        -0.0768      0.006    -13.178      0.000      -0.088      -0.065
ar.L30        -0.0597      0.004    -15.233      0.000      -0.067      -0.052
ma.L1         -0.9999      0.002   -531.634      0.000      -1.004      -0.996
sigma2         0.0715      0.000    323.364      0.000       0.071       0.072
===================================================================================
Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):            680920.91
Prob(Q):                              0.94   Prob(JB):                         0.00
Heteroskedasticity (H):              24.91   Skew:                             1.13
Prob(H) (two-sided):                  0.00   Kurtosis:                        33.14
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890    64.442217
17891    64.499146
17892    64.617654
17893    64.631578
17894    64.573736
17895    64.535947
17896    64.562677
17897    64.513164
17898    64.499930
17899    64.433314
Name: predicted_mean, dtype: float64
       lower price  upper price
17890    63.917981    64.966453
17891    63.744000    65.254293
17892    63.682013    65.553295
17893    63.534279    65.728877
17894    63.328933    65.818538
17895    63.150251    65.921642
17896    63.047298    66.078057
17897    62.871170    66.155159
17898    62.732143    66.267716
17899    62.538650    66.327979

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.808649516198752
Weighted Mean Absolute Percentage Error (WMAPE): 1.1550336196433288
In [730]:
forecastplusyahoo('PGSUS',30,1,3)

                            price short_name
timestamp                                   
2021-12-27 09:00:00+03:00   89.90      PGSUS
2021-12-27 10:00:00+03:00   91.65      PGSUS
2021-12-27 11:00:00+03:00   90.35      PGSUS
2021-12-27 12:00:00+03:00   90.15      PGSUS
2021-12-27 13:00:00+03:00   90.30      PGSUS
...                           ...        ...
2023-09-22 14:00:00+03:00  762.50      PGSUS
2023-09-22 15:00:00+03:00  761.60      PGSUS
2023-09-22 16:00:00+03:00  761.40      PGSUS
2023-09-22 17:00:00+03:00  766.20      PGSUS
2023-09-22 18:00:00+03:00  767.20      PGSUS

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for PGSUS.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                               price
2021-12-27 06:00:00+00:00  89.900000
2021-12-27 06:30:00+00:00  90.400002
2021-12-27 07:00:00+00:00  91.650000
2021-12-27 07:30:00+00:00  90.250000
2021-12-27 08:00:00+00:00  90.350000
2021-12-27 08:30:00+00:00  90.599998
2021-12-27 09:00:00+00:00  90.150000
2021-12-27 09:30:00+00:00  89.699997
2021-12-27 10:00:00+00:00  90.300000
2021-12-27 10:30:00+00:00  90.099998
2021-12-27 11:00:00+00:00  88.950000
2021-12-27 11:30:00+00:00  89.000000
2021-12-27 12:00:00+00:00  89.050000
2021-12-27 12:30:00+00:00  88.550003
2021-12-27 13:00:00+00:00  88.900000
2021-12-27 13:30:00+00:00  88.650002
2021-12-27 14:00:00+00:00  88.100000
2021-12-27 15:00:00+00:00  87.900000
2021-12-28 06:00:00+00:00  89.000000
2021-12-28 06:30:00+00:00  87.949997
2021-12-28 07:00:00+00:00  88.700000
2021-12-28 07:30:00+00:00  88.750000
2021-12-28 08:00:00+00:00  88.050000
2021-12-28 08:30:00+00:00  88.050003
2021-12-28 09:00:00+00:00  88.050000
2021-12-28 09:30:00+00:00  88.150002
2021-12-28 10:00:00+00:00  87.850000
2021-12-28 10:30:00+00:00  87.400002
2021-12-28 11:00:00+00:00  87.300000
2021-12-28 11:30:00+00:00  87.449997
2021-12-28 12:00:00+00:00  87.350000
2021-12-28 12:30:00+00:00  86.849998
2021-12-28 13:00:00+00:00  86.050000
2021-12-28 13:30:00+00:00  84.849998
2021-12-28 14:00:00+00:00  86.600000
2021-12-28 15:00:00+00:00  86.300000
2021-12-29 06:00:00+00:00  85.750000
2021-12-29 06:30:00+00:00  84.500000
2021-12-29 07:00:00+00:00  84.500000
2021-12-29 07:30:00+00:00  85.800003
2021-12-29 08:00:00+00:00  87.250000
2021-12-29 08:30:00+00:00  86.650002
2021-12-29 09:00:00+00:00  87.250000
2021-12-29 09:30:00+00:00  86.250000
2021-12-29 10:00:00+00:00  87.000000
2021-12-29 10:30:00+00:00  86.500000
2021-12-29 11:00:00+00:00  87.350000
2021-12-29 11:30:00+00:00  87.750000
2021-12-29 12:00:00+00:00  87.750000
2021-12-29 12:30:00+00:00  88.349998
                                price
2023-09-21 10:00:00+00:00  746.400000
2023-09-21 10:30:00+00:00  756.099976
2023-09-21 11:00:00+00:00  764.300000
2023-09-21 11:30:00+00:00  760.000000
2023-09-21 12:00:00+00:00  756.800000
2023-09-21 12:30:00+00:00  765.000000
2023-09-21 13:00:00+00:00  763.700000
2023-09-21 13:30:00+00:00  768.099976
2023-09-21 14:00:00+00:00  774.100000
2023-09-21 14:30:00+00:00  774.099976
2023-09-21 15:00:00+00:00  772.800000
2023-09-22 06:00:00+00:00  779.800000
2023-09-22 06:30:00+00:00  775.500000
2023-09-22 07:00:00+00:00  769.500000
2023-09-22 07:30:00+00:00  774.400024
2023-09-22 08:00:00+00:00  774.700000
2023-09-22 08:30:00+00:00  773.700012
2023-09-22 09:00:00+00:00  773.200000
2023-09-22 09:30:00+00:00  771.900024
2023-09-22 10:00:00+00:00  770.600000
2023-09-22 10:30:00+00:00  766.299988
2023-09-22 11:00:00+00:00  762.500000
2023-09-22 11:30:00+00:00  759.799988
2023-09-22 12:00:00+00:00  761.600000
2023-09-22 12:30:00+00:00  762.700012
2023-09-22 13:00:00+00:00  761.400000
2023-09-22 13:30:00+00:00  759.500000
2023-09-22 14:00:00+00:00  766.200000
2023-09-22 14:30:00+00:00  766.200012
2023-09-22 15:00:00+00:00  767.200000
Mean of the first 10 values: price    781.74
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  770.0
2023-09-25 10:00:00+03:00  763.0
2023-09-25 11:00:00+03:00  772.6
2023-09-25 12:00:00+03:00  781.6
2023-09-25 13:00:00+03:00  782.8
2023-09-25 14:00:00+03:00  778.9
2023-09-25 15:00:00+03:00  780.1
2023-09-25 16:00:00+03:00  787.4
2023-09-25 17:00:00+03:00  801.0
2023-09-25 18:00:00+03:00  800.0
                              price
2018-01-02 10:00:00+03:00  0.300000
2018-01-02 11:00:00+03:00  0.740000
2018-01-02 12:00:00+03:00  0.080000
2018-01-02 13:00:00+03:00 -0.020000
2018-01-02 14:00:00+03:00  0.200000
...                             ...
2023-09-22 13:00:00+00:00 -1.300012
2023-09-22 13:30:00+00:00 -1.900000
2023-09-22 14:00:00+00:00  6.700000
2023-09-22 14:30:00+00:00  0.000012
2023-09-22 15:00:00+00:00  0.999988

[17885 rows x 1 columns]
ADF Statistic: -21.812130999552586
p-value: 0.0
Critical Values: {'1%': -3.4307165848952463, '5%': -2.861702016559853, '10%': -2.566856237175401}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17886
Model:                ARIMA(30, 3, 1)   Log Likelihood              -39762.564
Date:                Sun, 24 Dec 2023   AIC                          79589.127
Time:                        21:51:21   BIC                          79838.459
Sample:                             0   HQIC                         79671.145
                              - 17886                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.9895      0.002   -423.444      0.000      -0.994      -0.985
ar.L2         -0.9942      0.004   -223.995      0.000      -1.003      -0.985
ar.L3         -0.9731      0.006   -154.602      0.000      -0.985      -0.961
ar.L4         -0.9366      0.007   -125.386      0.000      -0.951      -0.922
ar.L5         -0.8962      0.008   -106.130      0.000      -0.913      -0.880
ar.L6         -0.8418      0.009    -94.583      0.000      -0.859      -0.824
ar.L7         -0.8049      0.010    -83.596      0.000      -0.824      -0.786
ar.L8         -0.7667      0.010    -74.840      0.000      -0.787      -0.747
ar.L9         -0.7213      0.011    -67.078      0.000      -0.742      -0.700
ar.L10        -0.6640      0.011    -59.806      0.000      -0.686      -0.642
ar.L11        -0.6273      0.011    -54.570      0.000      -0.650      -0.605
ar.L12        -0.5978      0.012    -50.768      0.000      -0.621      -0.575
ar.L13        -0.5500      0.012    -45.925      0.000      -0.574      -0.527
ar.L14        -0.5100      0.012    -41.828      0.000      -0.534      -0.486
ar.L15        -0.4930      0.012    -40.464      0.000      -0.517      -0.469
ar.L16        -0.4542      0.012    -37.114      0.000      -0.478      -0.430
ar.L17        -0.4095      0.013    -32.137      0.000      -0.434      -0.385
ar.L18        -0.3555      0.012    -28.638      0.000      -0.380      -0.331
ar.L19        -0.3039      0.012    -24.850      0.000      -0.328      -0.280
ar.L20        -0.3183      0.012    -27.110      0.000      -0.341      -0.295
ar.L21        -0.3290      0.011    -29.255      0.000      -0.351      -0.307
ar.L22        -0.3322      0.011    -30.735      0.000      -0.353      -0.311
ar.L23        -0.2885      0.010    -27.691      0.000      -0.309      -0.268
ar.L24        -0.2635      0.010    -26.293      0.000      -0.283      -0.244
ar.L25        -0.1929      0.009    -20.398      0.000      -0.211      -0.174
ar.L26        -0.1336      0.009    -15.555      0.000      -0.150      -0.117
ar.L27        -0.0952      0.008    -12.129      0.000      -0.111      -0.080
ar.L28        -0.0823      0.007    -11.918      0.000      -0.096      -0.069
ar.L29        -0.0570      0.005    -11.042      0.000      -0.067      -0.047
ar.L30        -0.0336      0.003     -9.859      0.000      -0.040      -0.027
ma.L1         -1.0000      0.007   -140.925      0.000      -1.014      -0.986
sigma2         4.9906      0.032    154.919      0.000       4.927       5.054
===================================================================================
Ljung-Box (L1) (Q):                   0.02   Jarque-Bera (JB):           5895945.35
Prob(Q):                              0.88   Prob(JB):                         0.00
Heteroskedasticity (H):              53.76   Skew:                             2.92
Prob(H) (two-sided):                  0.00   Kurtosis:                        91.76
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17886    767.567744
17887    767.897003
17888    768.604571
17889    769.655875
17890    770.179944
17891    770.117911
17892    770.483798
17893    770.867781
17894    770.692424
17895    770.562294
Name: predicted_mean, dtype: float64
       lower price  upper price
17886   763.189170   771.946319
17887   761.671871   774.122135
17888   760.978522   776.230621
17889   760.802568   778.509182
17890   760.175417   780.184471
17891   759.004624   781.231198
17892   758.263741   782.703856
17893   757.566207   784.169355
17894   756.323032   785.061816
17895   755.118173   786.006416

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 16.04938089961804
Weighted Mean Absolute Percentage Error (WMAPE): 1.722789594582363
In [733]:
forecastplusyahoo('PETKM', 30, 1, 3)

                           price short_name
timestamp                                  
2021-12-27 09:00:00+03:00   8.15      PETKM
2021-12-27 10:00:00+03:00   8.39      PETKM
2021-12-27 11:00:00+03:00   8.25      PETKM
2021-12-27 12:00:00+03:00   8.19      PETKM
2021-12-27 13:00:00+03:00   8.18      PETKM
...                          ...        ...
2023-09-22 14:00:00+03:00  19.75      PETKM
2023-09-22 15:00:00+03:00  19.77      PETKM
2023-09-22 16:00:00+03:00  19.80      PETKM
2023-09-22 17:00:00+03:00  19.95      PETKM
2023-09-22 18:00:00+03:00  19.90      PETKM

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for PETKM.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                           price
2021-12-27 06:00:00+00:00   8.15
2021-12-27 06:30:00+00:00   8.28
2021-12-27 07:00:00+00:00   8.39
2021-12-27 07:30:00+00:00   8.26
2021-12-27 08:00:00+00:00   8.25
2021-12-27 08:30:00+00:00   8.22
2021-12-27 09:00:00+00:00   8.19
2021-12-27 09:30:00+00:00   8.15
2021-12-27 10:00:00+00:00   8.18
2021-12-27 10:30:00+00:00   8.16
2021-12-27 11:00:00+00:00   8.12
2021-12-27 11:30:00+00:00   8.02
2021-12-27 12:00:00+00:00   8.06
2021-12-27 12:30:00+00:00   8.04
2021-12-27 13:00:00+00:00   8.06
2021-12-27 13:30:00+00:00   8.11
2021-12-27 14:00:00+00:00   8.01
2021-12-27 15:00:00+00:00   8.01
2021-12-28 06:00:00+00:00   8.12
2021-12-28 06:30:00+00:00   8.04
2021-12-28 07:00:00+00:00   8.06
2021-12-28 07:30:00+00:00   8.01
2021-12-28 08:00:00+00:00   7.97
2021-12-28 08:30:00+00:00   7.97
2021-12-28 09:00:00+00:00   7.93
2021-12-28 09:30:00+00:00   7.96
2021-12-28 10:00:00+00:00   7.95
2021-12-28 10:30:00+00:00   7.93
2021-12-28 11:00:00+00:00   7.88
2021-12-28 11:30:00+00:00   7.92
2021-12-28 12:00:00+00:00   7.92
2021-12-28 12:30:00+00:00   7.93
2021-12-28 13:00:00+00:00   7.90
2021-12-28 13:30:00+00:00   7.72
2021-12-28 14:00:00+00:00   7.84
2021-12-28 15:00:00+00:00   7.82
2021-12-29 06:00:00+00:00   7.73
2021-12-29 06:30:00+00:00   7.72
2021-12-29 07:00:00+00:00   7.77
2021-12-29 07:30:00+00:00   7.85
2021-12-29 08:00:00+00:00   7.86
2021-12-29 08:30:00+00:00   7.87
2021-12-29 09:00:00+00:00   7.88
2021-12-29 09:30:00+00:00   7.89
2021-12-29 10:00:00+00:00   7.94
2021-12-29 10:30:00+00:00   7.89
2021-12-29 11:00:00+00:00   7.95
2021-12-29 11:30:00+00:00   7.99
2021-12-29 12:00:00+00:00   7.99
2021-12-29 12:30:00+00:00   8.05
                               price
2023-09-21 10:00:00+00:00  18.740000
2023-09-21 10:30:00+00:00  19.030001
2023-09-21 11:00:00+00:00  19.180000
2023-09-21 11:30:00+00:00  19.160000
2023-09-21 12:00:00+00:00  19.140000
2023-09-21 12:30:00+00:00  19.430000
2023-09-21 13:00:00+00:00  19.430000
2023-09-21 13:30:00+00:00  19.570000
2023-09-21 14:00:00+00:00  19.690000
2023-09-21 14:30:00+00:00  19.690001
2023-09-21 15:00:00+00:00  19.690000
2023-09-22 06:00:00+00:00  19.700000
2023-09-22 06:30:00+00:00  19.980000
2023-09-22 07:00:00+00:00  20.080000
2023-09-22 07:30:00+00:00  20.059999
2023-09-22 08:00:00+00:00  20.160000
2023-09-22 08:30:00+00:00  20.020000
2023-09-22 09:00:00+00:00  20.040000
2023-09-22 09:30:00+00:00  20.020000
2023-09-22 10:00:00+00:00  19.910000
2023-09-22 10:30:00+00:00  19.670000
2023-09-22 11:00:00+00:00  19.750000
2023-09-22 11:30:00+00:00  19.650000
2023-09-22 12:00:00+00:00  19.770000
2023-09-22 12:30:00+00:00  19.809999
2023-09-22 13:00:00+00:00  19.800000
2023-09-22 13:30:00+00:00  19.709999
2023-09-22 14:00:00+00:00  19.950000
2023-09-22 14:30:00+00:00  19.950001
2023-09-22 15:00:00+00:00  19.900000
Mean of the first 10 values: price    20.306
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  20.10
2023-09-25 10:00:00+03:00  20.22
2023-09-25 11:00:00+03:00  20.26
2023-09-25 12:00:00+03:00  20.26
2023-09-25 13:00:00+03:00  20.40
2023-09-25 14:00:00+03:00  20.30
2023-09-25 15:00:00+03:00  20.34
2023-09-25 16:00:00+03:00  20.40
2023-09-25 17:00:00+03:00  20.40
2023-09-25 18:00:00+03:00  20.38
                                  price
2018-01-02 10:00:00+03:00  3.380000e-02
2018-01-02 11:00:00+03:00  1.120000e-02
2018-01-02 12:00:00+03:00  5.600000e-03
2018-01-02 13:00:00+03:00  5.600000e-03
2018-01-02 14:00:00+03:00  2.820000e-02
...                                 ...
2023-09-22 13:00:00+00:00 -9.999466e-03
2023-09-22 13:30:00+00:00 -9.000092e-02
2023-09-22 14:00:00+00:00  2.400009e-01
2023-09-22 14:30:00+00:00  7.629395e-07
2023-09-22 15:00:00+00:00 -5.000076e-02

[17890 rows x 1 columns]
ADF Statistic: -20.941724628552823
p-value: 0.0
Critical Values: {'1%': -3.430716400035936, '5%': -2.8617019348641204, '10%': -2.566856193690025}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17891
Model:                ARIMA(30, 3, 1)   Log Likelihood               21557.981
Date:                Sun, 24 Dec 2023   AIC                         -43051.962
Time:                        21:58:43   BIC                         -42802.622
Sample:                             0   HQIC                        -42969.943
                              - 17891                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.0301      0.004   -246.202      0.000      -1.038      -1.022
ar.L2         -0.9590      0.007   -137.228      0.000      -0.973      -0.945
ar.L3         -0.9755      0.010   -100.424      0.000      -0.995      -0.956
ar.L4         -0.9128      0.012    -77.518      0.000      -0.936      -0.890
ar.L5         -0.9239      0.014    -68.419      0.000      -0.950      -0.897
ar.L6         -0.8403      0.015    -55.136      0.000      -0.870      -0.810
ar.L7         -0.8553      0.016    -52.087      0.000      -0.887      -0.823
ar.L8         -0.7804      0.017    -44.685      0.000      -0.815      -0.746
ar.L9         -0.7662      0.018    -42.283      0.000      -0.802      -0.731
ar.L10        -0.7117      0.019    -37.900      0.000      -0.749      -0.675
ar.L11        -0.6563      0.019    -34.040      0.000      -0.694      -0.619
ar.L12        -0.6369      0.019    -33.126      0.000      -0.675      -0.599
ar.L13        -0.5921      0.019    -30.791      0.000      -0.630      -0.554
ar.L14        -0.5979      0.019    -30.773      0.000      -0.636      -0.560
ar.L15        -0.5363      0.019    -27.536      0.000      -0.575      -0.498
ar.L16        -0.5274      0.019    -27.220      0.000      -0.565      -0.489
ar.L17        -0.4306      0.019    -22.472      0.000      -0.468      -0.393
ar.L18        -0.4427      0.019    -23.587      0.000      -0.480      -0.406
ar.L19        -0.3638      0.018    -19.793      0.000      -0.400      -0.328
ar.L20        -0.4008      0.018    -22.584      0.000      -0.436      -0.366
ar.L21        -0.3816      0.017    -22.460      0.000      -0.415      -0.348
ar.L22        -0.4229      0.017    -25.578      0.000      -0.455      -0.390
ar.L23        -0.3234      0.016    -20.417      0.000      -0.354      -0.292
ar.L24        -0.3493      0.015    -23.904      0.000      -0.378      -0.321
ar.L25        -0.2654      0.013    -19.670      0.000      -0.292      -0.239
ar.L26        -0.2706      0.012    -22.470      0.000      -0.294      -0.247
ar.L27        -0.1783      0.011    -16.738      0.000      -0.199      -0.157
ar.L28        -0.1926      0.009    -21.358      0.000      -0.210      -0.175
ar.L29        -0.0921      0.007    -12.315      0.000      -0.107      -0.077
ar.L30        -0.0913      0.005    -18.027      0.000      -0.101      -0.081
ma.L1         -0.9444      0.003   -306.574      0.000      -0.950      -0.938
sigma2         0.0053   1.73e-05    305.221      0.000       0.005       0.005
===================================================================================
Ljung-Box (L1) (Q):                   7.81   Jarque-Bera (JB):            591350.37
Prob(Q):                              0.01   Prob(JB):                         0.00
Heteroskedasticity (H):              19.07   Skew:                             0.64
Prob(H) (two-sided):                  0.00   Kurtosis:                        31.14
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891    19.941735
17892    19.976421
17893    20.026530
17894    20.033310
17895    20.072177
17896    20.077084
17897    20.130767
17898    20.126092
17899    20.191097
17900    20.178801
Name: predicted_mean, dtype: float64
       lower price  upper price
17891    19.799282    20.084188
17892    19.772369    20.180473
17893    19.764723    20.288337
17894    19.721516    20.345104
17895    19.708484    20.435871
17896    19.665183    20.488985
17897    19.666004    20.595531
17898    19.611462    20.640722
17899    19.621823    20.760371
17900    19.555704    20.801899

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.234597404703211
Weighted Mean Absolute Percentage Error (WMAPE): 1.1579513037803237
In [740]:
forecastplusyahoo('SAHOL', 30, 1, 3)

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00  12.4338      SAHOL
2021-12-27 10:00:00+03:00  12.5345      SAHOL
2021-12-27 11:00:00+03:00  12.4978      SAHOL
2021-12-27 12:00:00+03:00  12.4978      SAHOL
2021-12-27 13:00:00+03:00  12.5985      SAHOL
...                            ...        ...
2023-09-22 14:00:00+03:00  56.5000      SAHOL
2023-09-22 15:00:00+03:00  56.5000      SAHOL
2023-09-22 16:00:00+03:00  56.7000      SAHOL
2023-09-22 17:00:00+03:00  56.4000      SAHOL
2023-09-22 18:00:00+03:00  56.5000      SAHOL

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for SAHOL.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                             price
2021-12-27 06:00:00+00:00  12.4338
2021-12-27 06:30:00+00:00  13.6500
2021-12-27 07:00:00+00:00  12.5345
2021-12-27 07:30:00+00:00  13.7000
2021-12-27 08:00:00+00:00  12.4978
2021-12-27 08:30:00+00:00  13.6900
2021-12-27 09:00:00+00:00  12.4978
2021-12-27 09:30:00+00:00  13.6700
2021-12-27 10:00:00+00:00  12.5985
2021-12-27 10:30:00+00:00  13.6800
2021-12-27 11:00:00+00:00  12.4155
2021-12-27 11:30:00+00:00  13.4600
2021-12-27 12:00:00+00:00  12.3606
2021-12-27 12:30:00+00:00  13.4200
2021-12-27 13:00:00+00:00  12.3240
2021-12-27 13:30:00+00:00  13.5100
2021-12-27 14:00:00+00:00  12.2416
2021-12-27 15:00:00+00:00  12.2325
2021-12-28 06:00:00+00:00  12.3514
2021-12-28 06:30:00+00:00  13.4200
2021-12-28 07:00:00+00:00  12.3148
2021-12-28 07:30:00+00:00  13.4400
2021-12-28 08:00:00+00:00  12.2050
2021-12-28 08:30:00+00:00  13.2700
2021-12-28 09:00:00+00:00  12.1410
2021-12-28 09:30:00+00:00  13.2400
2021-12-28 10:00:00+00:00  12.0587
2021-12-28 10:30:00+00:00  13.1300
2021-12-28 11:00:00+00:00  11.9489
2021-12-28 11:30:00+00:00  13.1500
2021-12-28 12:00:00+00:00  12.0495
2021-12-28 12:30:00+00:00  13.1900
2021-12-28 13:00:00+00:00  11.9763
2021-12-28 13:30:00+00:00  12.9200
2021-12-28 14:00:00+00:00  11.9214
2021-12-28 15:00:00+00:00  11.9123
2021-12-29 06:00:00+00:00  11.8574
2021-12-29 06:30:00+00:00  12.8700
2021-12-29 07:00:00+00:00  11.7934
2021-12-29 07:30:00+00:00  13.0400
2021-12-29 08:00:00+00:00  11.9672
2021-12-29 08:30:00+00:00  13.0600
2021-12-29 09:00:00+00:00  12.0038
2021-12-29 09:30:00+00:00  13.1300
2021-12-29 10:00:00+00:00  12.0403
2021-12-29 10:30:00+00:00  13.0800
2021-12-29 11:00:00+00:00  12.0221
2021-12-29 11:30:00+00:00  13.2100
2021-12-29 12:00:00+00:00  12.1135
2021-12-29 12:30:00+00:00  13.4000
                               price
2023-09-21 10:00:00+00:00  55.750000
2023-09-21 10:30:00+00:00  55.900002
2023-09-21 11:00:00+00:00  56.450000
2023-09-21 11:30:00+00:00  56.299999
2023-09-21 12:00:00+00:00  56.450000
2023-09-21 12:30:00+00:00  56.900002
2023-09-21 13:00:00+00:00  56.600000
2023-09-21 13:30:00+00:00  56.799999
2023-09-21 14:00:00+00:00  56.900000
2023-09-21 14:30:00+00:00  56.900002
2023-09-21 15:00:00+00:00  56.950000
2023-09-22 06:00:00+00:00  57.400000
2023-09-22 06:30:00+00:00  57.299999
2023-09-22 07:00:00+00:00  57.050000
2023-09-22 07:30:00+00:00  57.000000
2023-09-22 08:00:00+00:00  56.850000
2023-09-22 08:30:00+00:00  56.900002
2023-09-22 09:00:00+00:00  56.950000
2023-09-22 09:30:00+00:00  56.849998
2023-09-22 10:00:00+00:00  56.950000
2023-09-22 10:30:00+00:00  56.650002
2023-09-22 11:00:00+00:00  56.500000
2023-09-22 11:30:00+00:00  56.299999
2023-09-22 12:00:00+00:00  56.500000
2023-09-22 12:30:00+00:00  56.799999
2023-09-22 13:00:00+00:00  56.700000
2023-09-22 13:30:00+00:00  56.299999
2023-09-22 14:00:00+00:00  56.400000
2023-09-22 14:30:00+00:00  56.400002
2023-09-22 15:00:00+00:00  56.500000
Mean of the first 10 values: price    57.66
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  56.75
2023-09-25 10:00:00+03:00  57.40
2023-09-25 11:00:00+03:00  57.45
2023-09-25 12:00:00+03:00  57.75
2023-09-25 13:00:00+03:00  57.90
2023-09-25 14:00:00+03:00  57.75
2023-09-25 15:00:00+03:00  57.60
2023-09-25 16:00:00+03:00  57.95
2023-09-25 17:00:00+03:00  57.95
2023-09-25 18:00:00+03:00  58.10
                              price
2018-01-02 10:00:00+03:00  0.000000
2018-01-02 11:00:00+03:00  0.063000
2018-01-02 12:00:00+03:00 -0.031500
2018-01-02 13:00:00+03:00  0.023600
2018-01-02 14:00:00+03:00  0.015700
...                             ...
2023-09-22 13:00:00+00:00 -0.099999
2023-09-22 13:30:00+00:00 -0.400001
2023-09-22 14:00:00+00:00  0.100001
2023-09-22 14:30:00+00:00  0.000002
2023-09-22 15:00:00+00:00  0.099998

[17889 rows x 1 columns]
ADF Statistic: -21.755710529704338
p-value: 0.0
Critical Values: {'1%': -3.4307165027125293, '5%': -2.861701980240464, '10%': -2.5668562178431515}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17890
Model:                ARIMA(30, 3, 1)   Log Likelihood               -2969.986
Date:                Sun, 24 Dec 2023   AIC                           6003.972
Time:                        22:16:28   BIC                           6253.310
Sample:                             0   HQIC                          6085.991
                              - 17890                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.6593      0.004   -378.645      0.000      -1.668      -1.651
ar.L2         -1.6006      0.010   -165.187      0.000      -1.620      -1.582
ar.L3         -1.6381      0.014   -113.783      0.000      -1.666      -1.610
ar.L4         -1.5950      0.018    -86.726      0.000      -1.631      -1.559
ar.L5         -1.5562      0.022    -70.010      0.000      -1.600      -1.513
ar.L6         -1.4618      0.026    -57.182      0.000      -1.512      -1.412
ar.L7         -1.4311      0.028    -51.153      0.000      -1.486      -1.376
ar.L8         -1.3870      0.030    -46.442      0.000      -1.445      -1.328
ar.L9         -1.3640      0.032    -43.143      0.000      -1.426      -1.302
ar.L10        -1.2635      0.033    -37.988      0.000      -1.329      -1.198
ar.L11        -1.1918      0.035    -34.423      0.000      -1.260      -1.124
ar.L12        -1.1463      0.036    -32.252      0.000      -1.216      -1.077
ar.L13        -1.0614      0.036    -29.588      0.000      -1.132      -0.991
ar.L14        -1.0159      0.036    -28.424      0.000      -1.086      -0.946
ar.L15        -0.9727      0.036    -27.303      0.000      -1.043      -0.903
ar.L16        -1.0455      0.035    -29.538      0.000      -1.115      -0.976
ar.L17        -1.0822      0.035    -30.604      0.000      -1.152      -1.013
ar.L18        -0.6771      0.036    -18.951      0.000      -0.747      -0.607
ar.L19        -0.2892      0.035     -8.262      0.000      -0.358      -0.221
ar.L20        -0.4053      0.033    -12.117      0.000      -0.471      -0.340
ar.L21        -0.4374      0.032    -13.629      0.000      -0.500      -0.375
ar.L22        -0.4050      0.031    -13.242      0.000      -0.465      -0.345
ar.L23        -0.4031      0.029    -14.001      0.000      -0.460      -0.347
ar.L24        -0.3492      0.027    -13.102      0.000      -0.401      -0.297
ar.L25        -0.3026      0.024    -12.362      0.000      -0.351      -0.255
ar.L26        -0.1883      0.022     -8.677      0.000      -0.231      -0.146
ar.L27        -0.1244      0.018     -6.739      0.000      -0.161      -0.088
ar.L28        -0.0703      0.015     -4.755      0.000      -0.099      -0.041
ar.L29        -0.0556      0.011     -5.011      0.000      -0.077      -0.034
ar.L30         0.0289      0.006      5.137      0.000       0.018       0.040
ma.L1         -0.9547      0.003   -307.651      0.000      -0.961      -0.949
sigma2         0.0809      0.000    225.706      0.000       0.080       0.082
===================================================================================
Ljung-Box (L1) (Q):                   0.03   Jarque-Bera (JB):            152125.23
Prob(Q):                              0.86   Prob(JB):                         0.00
Heteroskedasticity (H):              48.87   Skew:                            -0.03
Prob(H) (two-sided):                  0.00   Kurtosis:                        17.29
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890    56.569994
17891    56.335954
17892    56.338118
17893    56.229987
17894    56.291149
17895    56.309717
17896    56.225383
17897    56.301704
17898    56.166691
17899    55.986905
Name: predicted_mean, dtype: float64
       lower price  upper price
17890    56.012651    57.127337
17891    55.738531    56.933378
17892    55.559926    57.116310
17893    55.397390    57.062585
17894    55.315654    57.266645
17895    55.263555    57.355880
17896    55.038439    57.412326
17897    55.036884    57.566524
17898    54.766125    57.567256
17899    54.501757    57.472054

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 1.470576171892774
Weighted Mean Absolute Percentage Error (WMAPE): 2.401040004672276
In [735]:
forecastplusyahoo('SASA', 30, 1, 1)

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00  10.5956       SASA
2021-12-27 10:00:00+03:00  11.0217       SASA
2021-12-27 11:00:00+03:00  11.0108       SASA
2021-12-27 12:00:00+03:00  11.0326       SASA
2021-12-27 13:00:00+03:00  11.0108       SASA
...                            ...        ...
2023-09-22 14:00:00+03:00  45.6800       SASA
2023-09-22 15:00:00+03:00  45.5400       SASA
2023-09-22 16:00:00+03:00  45.5600       SASA
2023-09-22 17:00:00+03:00  45.5200       SASA
2023-09-22 18:00:00+03:00  45.5200       SASA

[4322 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for SASA.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                               price
2021-12-27 06:00:00+00:00  10.595600
2021-12-27 06:30:00+00:00  10.840745
2021-12-27 07:00:00+00:00  11.021700
2021-12-27 07:30:00+00:00  10.733411
2021-12-27 08:00:00+00:00  11.010800
2021-12-27 08:30:00+00:00  10.915878
2021-12-27 09:00:00+00:00  11.032600
2021-12-27 09:30:00+00:00  10.787078
2021-12-27 10:00:00+00:00  11.010800
2021-12-27 10:30:00+00:00  10.851479
2021-12-27 11:00:00+00:00  10.934800
2021-12-27 11:30:00+00:00  10.711944
2021-12-27 12:00:00+00:00  10.869600
2021-12-27 12:30:00+00:00  10.703357
2021-12-27 13:00:00+00:00  11.000000
2021-12-27 13:30:00+00:00  11.044680
2021-12-27 14:00:00+00:00  11.119600
2021-12-27 15:00:00+00:00  11.163000
2021-12-28 06:00:00+00:00  11.391300
2021-12-28 06:30:00+00:00  11.270082
2021-12-28 07:00:00+00:00  11.423900
2021-12-28 07:30:00+00:00  11.152014
2021-12-28 08:00:00+00:00  11.260800
2021-12-28 08:30:00+00:00  11.141280
2021-12-28 09:00:00+00:00  11.228300
2021-12-28 09:30:00+00:00  11.152014
2021-12-28 10:00:00+00:00  11.195700
2021-12-28 10:30:00+00:00  11.055413
2021-12-28 11:00:00+00:00  11.173900
2021-12-28 11:30:00+00:00  11.066146
2021-12-28 12:00:00+00:00  11.184800
2021-12-28 12:30:00+00:00  11.044680
2021-12-28 13:00:00+00:00  11.217300
2021-12-28 13:30:00+00:00  10.905145
2021-12-28 14:00:00+00:00  11.402200
2021-12-28 15:00:00+00:00  11.478300
2021-12-29 06:00:00+00:00  11.576100
2021-12-29 06:30:00+00:00  11.270082
2021-12-29 07:00:00+00:00  11.445700
2021-12-29 07:30:00+00:00  11.420349
2021-12-29 08:00:00+00:00  11.500000
2021-12-29 08:30:00+00:00  11.323749
2021-12-29 09:00:00+00:00  11.500000
2021-12-29 09:30:00+00:00  11.323749
2021-12-29 10:00:00+00:00  11.489100
2021-12-29 10:30:00+00:00  11.355948
2021-12-29 11:00:00+00:00  11.543500
2021-12-29 11:30:00+00:00  11.355948
2021-12-29 12:00:00+00:00  11.478300
2021-12-29 12:30:00+00:00  11.377416
                               price
2023-09-21 10:00:00+00:00  44.520000
2023-09-21 10:30:00+00:00  45.020000
2023-09-21 11:00:00+00:00  45.640000
2023-09-21 11:30:00+00:00  45.500000
2023-09-21 12:00:00+00:00  45.520000
2023-09-21 12:30:00+00:00  45.919998
2023-09-21 13:00:00+00:00  45.780000
2023-09-21 13:30:00+00:00  46.000000
2023-09-21 14:00:00+00:00  46.260000
2023-09-21 14:30:00+00:00  46.259998
2023-09-21 15:00:00+00:00  46.300000
2023-09-22 06:00:00+00:00  46.300000
2023-09-22 06:30:00+00:00  46.419998
2023-09-22 07:00:00+00:00  46.340000
2023-09-22 07:30:00+00:00  46.320000
2023-09-22 08:00:00+00:00  46.440000
2023-09-22 08:30:00+00:00  46.320000
2023-09-22 09:00:00+00:00  46.200000
2023-09-22 09:30:00+00:00  46.160000
2023-09-22 10:00:00+00:00  45.920000
2023-09-22 10:30:00+00:00  45.700001
2023-09-22 11:00:00+00:00  45.680000
2023-09-22 11:30:00+00:00  45.500000
2023-09-22 12:00:00+00:00  45.540000
2023-09-22 12:30:00+00:00  45.639999
2023-09-22 13:00:00+00:00  45.560000
2023-09-22 13:30:00+00:00  45.380001
2023-09-22 14:00:00+00:00  45.520000
2023-09-22 14:30:00+00:00  45.520000
2023-09-22 15:00:00+00:00  45.520000
Mean of the first 10 values: price    45.648
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  45.60
2023-09-25 10:00:00+03:00  45.28
2023-09-25 11:00:00+03:00  45.54
2023-09-25 12:00:00+03:00  45.66
2023-09-25 13:00:00+03:00  45.54
2023-09-25 14:00:00+03:00  45.46
2023-09-25 15:00:00+03:00  45.66
2023-09-25 16:00:00+03:00  45.92
2023-09-25 17:00:00+03:00  45.92
2023-09-25 18:00:00+03:00  45.90
                                  price
2018-01-02 10:00:00+03:00  6.900000e-03
2018-01-02 11:00:00+03:00  2.770000e-02
2018-01-02 12:00:00+03:00  1.930000e-02
2018-01-02 13:00:00+03:00  0.000000e+00
2018-01-02 14:00:00+03:00  0.000000e+00
...                                 ...
2023-09-22 13:00:00+00:00 -7.999939e-02
2023-09-22 13:30:00+00:00 -1.799989e-01
2023-09-22 14:00:00+00:00  1.399989e-01
2023-09-22 14:30:00+00:00  4.577637e-07
2023-09-22 15:00:00+00:00 -4.577637e-07

[17887 rows x 1 columns]
ADF Statistic: -20.598576779626608
p-value: 0.0
Critical Values: {'1%': -3.4307165232547536, '5%': -2.8617019893187847, '10%': -2.5668562226754013}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17888
Model:                ARIMA(30, 1, 1)   Log Likelihood               -4762.682
Date:                Sun, 24 Dec 2023   AIC                           9589.363
Time:                        22:04:22   BIC                           9838.702
Sample:                             0   HQIC                          9671.382
                              - 17888                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1017      0.004   -261.275      0.000      -1.110      -1.093
ar.L2         -0.1216      0.005    -25.825      0.000      -0.131      -0.112
ar.L3         -0.0341      0.005     -6.279      0.000      -0.045      -0.023
ar.L4         -0.0398      0.006     -7.153      0.000      -0.051      -0.029
ar.L5         -0.0207      0.005     -4.379      0.000      -0.030      -0.011
ar.L6         -0.0286      0.005     -5.902      0.000      -0.038      -0.019
ar.L7         -0.0129      0.005     -2.369      0.018      -0.024      -0.002
ar.L8          0.0025      0.007      0.370      0.712      -0.011       0.016
ar.L9          0.0386      0.007      5.495      0.000       0.025       0.052
ar.L10         0.0497      0.007      7.501      0.000       0.037       0.063
ar.L11         0.0325      0.006      5.040      0.000       0.020       0.045
ar.L12        -0.0015      0.006     -0.252      0.801      -0.013       0.010
ar.L13         0.0126      0.005      2.303      0.021       0.002       0.023
ar.L14        -0.0046      0.005     -0.853      0.394      -0.015       0.006
ar.L15         0.0202      0.006      3.503      0.000       0.009       0.031
ar.L16         0.0586      0.006     10.492      0.000       0.048       0.070
ar.L17         0.1060      0.004     29.715      0.000       0.099       0.113
ar.L18         0.2468      0.002    103.409      0.000       0.242       0.251
ar.L19         0.1875      0.002     78.199      0.000       0.183       0.192
ar.L20        -0.0681      0.004    -19.044      0.000      -0.075      -0.061
ar.L21        -0.0510      0.005    -10.439      0.000      -0.061      -0.041
ar.L22        -0.0312      0.004     -7.038      0.000      -0.040      -0.023
ar.L23        -0.0635      0.005    -11.993      0.000      -0.074      -0.053
ar.L24        -0.0284      0.005     -5.319      0.000      -0.039      -0.018
ar.L25        -0.0519      0.005    -10.174      0.000      -0.062      -0.042
ar.L26        -0.0010      0.006     -0.157      0.875      -0.013       0.011
ar.L27        -0.0068      0.006     -1.221      0.222      -0.018       0.004
ar.L28        -0.0330      0.006     -5.195      0.000      -0.045      -0.021
ar.L29        -0.0171      0.006     -2.840      0.005      -0.029      -0.005
ar.L30         0.0317      0.004      8.802      0.000       0.025       0.039
ma.L1          0.9059      0.003    272.405      0.000       0.899       0.912
sigma2         0.0997      0.000    492.330      0.000       0.099       0.100
===================================================================================
Ljung-Box (L1) (Q):                   0.04   Jarque-Bera (JB):          15877988.14
Prob(Q):                              0.83   Prob(JB):                         0.00
Heteroskedasticity (H):            1721.76   Skew:                            -0.99
Prob(H) (two-sided):                  0.00   Kurtosis:                       148.95
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17888    45.472006
17889    45.506526
17890    45.441232
17891    45.478445
17892    45.418288
17893    45.389174
17894    45.342015
17895    45.316609
17896    45.262937
17897    45.290115
Name: predicted_mean, dtype: float64
       lower price  upper price
17888    44.853236    46.090775
17889    44.712473    46.300578
17890    44.471954    46.410509
17891    44.394431    46.562460
17892    44.209232    46.627344
17893    44.088745    46.689602
17894    43.941464    46.742567
17895    43.837090    46.796127
17896    43.693951    46.831923
17897    43.642724    46.937507

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.37404949455119474
Weighted Mean Absolute Percentage Error (WMAPE): 0.6144695962537068
In [736]:
forecastplusyahoo('SISE', 30, 1, 1)

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00  13.3510       SISE
2021-12-27 10:00:00+03:00  13.5826       SISE
2021-12-27 11:00:00+03:00  13.4379       SISE
2021-12-27 12:00:00+03:00  13.4282       SISE
2021-12-27 13:00:00+03:00  13.4186       SISE
...                            ...        ...
2023-09-22 14:00:00+03:00  52.9000       SISE
2023-09-22 15:00:00+03:00  52.7500       SISE
2023-09-22 16:00:00+03:00  53.3000       SISE
2023-09-22 17:00:00+03:00  54.7000       SISE
2023-09-22 18:00:00+03:00  54.7000       SISE

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for SISE.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                             price
2021-12-27 06:00:00+00:00  13.3510
2021-12-27 06:30:00+00:00  14.0000
2021-12-27 07:00:00+00:00  13.5826
2021-12-27 07:30:00+00:00  13.9200
2021-12-27 08:00:00+00:00  13.4379
2021-12-27 08:30:00+00:00  13.9600
2021-12-27 09:00:00+00:00  13.4282
2021-12-27 09:30:00+00:00  13.8500
2021-12-27 10:00:00+00:00  13.4186
2021-12-27 10:30:00+00:00  13.8800
2021-12-27 11:00:00+00:00  13.3028
2021-12-27 11:30:00+00:00  13.6400
2021-12-27 12:00:00+00:00  13.1870
2021-12-27 12:30:00+00:00  13.6400
2021-12-27 13:00:00+00:00  13.2352
2021-12-27 13:30:00+00:00  13.7800
2021-12-27 14:00:00+00:00  13.1099
2021-12-27 15:00:00+00:00  13.0712
2021-12-28 06:00:00+00:00  13.3221
2021-12-28 06:30:00+00:00  13.7800
2021-12-28 07:00:00+00:00  13.4861
2021-12-28 07:30:00+00:00  13.8700
2021-12-28 08:00:00+00:00  13.4282
2021-12-28 08:30:00+00:00  13.9200
2021-12-28 09:00:00+00:00  13.3896
2021-12-28 09:30:00+00:00  13.8700
2021-12-28 10:00:00+00:00  13.3606
2021-12-28 10:30:00+00:00  13.7900
2021-12-28 11:00:00+00:00  13.1870
2021-12-28 11:30:00+00:00  13.7800
2021-12-28 12:00:00+00:00  13.2739
2021-12-28 12:30:00+00:00  13.7500
2021-12-28 13:00:00+00:00  13.1581
2021-12-28 13:30:00+00:00  13.3300
2021-12-28 14:00:00+00:00  12.9941
2021-12-28 15:00:00+00:00  12.9363
2021-12-29 06:00:00+00:00  12.9265
2021-12-29 06:30:00+00:00  13.4000
2021-12-29 07:00:00+00:00  12.9169
2021-12-29 07:30:00+00:00  13.5300
2021-12-29 08:00:00+00:00  13.1677
2021-12-29 08:30:00+00:00  13.6000
2021-12-29 09:00:00+00:00  13.2256
2021-12-29 09:30:00+00:00  13.6200
2021-12-29 10:00:00+00:00  13.2063
2021-12-29 10:30:00+00:00  13.6300
2021-12-29 11:00:00+00:00  13.2063
2021-12-29 11:30:00+00:00  13.7800
2021-12-29 12:00:00+00:00  13.3124
2021-12-29 12:30:00+00:00  13.8600
                               price
2023-09-21 10:00:00+00:00  51.250000
2023-09-21 10:30:00+00:00  51.900002
2023-09-21 11:00:00+00:00  52.400000
2023-09-21 11:30:00+00:00  52.500000
2023-09-21 12:00:00+00:00  52.200000
2023-09-21 12:30:00+00:00  52.349998
2023-09-21 13:00:00+00:00  52.450000
2023-09-21 13:30:00+00:00  52.750000
2023-09-21 14:00:00+00:00  53.150000
2023-09-21 14:30:00+00:00  53.150002
2023-09-21 15:00:00+00:00  52.950000
2023-09-22 06:00:00+00:00  53.750000
2023-09-22 06:30:00+00:00  53.750000
2023-09-22 07:00:00+00:00  53.650000
2023-09-22 07:30:00+00:00  53.500000
2023-09-22 08:00:00+00:00  53.550000
2023-09-22 08:30:00+00:00  53.400002
2023-09-22 09:00:00+00:00  53.550000
2023-09-22 09:30:00+00:00  53.500000
2023-09-22 10:00:00+00:00  53.500000
2023-09-22 10:30:00+00:00  53.299999
2023-09-22 11:00:00+00:00  52.900000
2023-09-22 11:30:00+00:00  52.700001
2023-09-22 12:00:00+00:00  52.750000
2023-09-22 12:30:00+00:00  53.200001
2023-09-22 13:00:00+00:00  53.300000
2023-09-22 13:30:00+00:00  53.099998
2023-09-22 14:00:00+00:00  54.700000
2023-09-22 14:30:00+00:00  54.700001
2023-09-22 15:00:00+00:00  54.700000
Mean of the first 10 values: price    55.305
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  54.80
2023-09-25 10:00:00+03:00  55.40
2023-09-25 11:00:00+03:00  55.55
2023-09-25 12:00:00+03:00  55.40
2023-09-25 13:00:00+03:00  55.45
2023-09-25 14:00:00+03:00  55.15
2023-09-25 15:00:00+03:00  55.30
2023-09-25 16:00:00+03:00  55.55
2023-09-25 17:00:00+03:00  55.25
2023-09-25 18:00:00+03:00  55.20
                                  price
2018-01-02 10:00:00+03:00 -5.130000e-02
2018-01-02 11:00:00+03:00  2.560000e-02
2018-01-02 12:00:00+03:00  0.000000e+00
2018-01-02 13:00:00+03:00  0.000000e+00
2018-01-02 14:00:00+03:00  2.570000e-02
...                                 ...
2023-09-22 13:00:00+00:00  9.999924e-02
2023-09-22 13:30:00+00:00 -2.000015e-01
2023-09-22 14:00:00+00:00  1.600002e+00
2023-09-22 14:30:00+00:00  7.629395e-07
2023-09-22 15:00:00+00:00 -7.629395e-07

[17889 rows x 1 columns]
ADF Statistic: -21.263395625979722
p-value: 0.0
Critical Values: {'1%': -3.4307165027125293, '5%': -2.861701980240464, '10%': -2.5668562178431515}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17890
Model:                ARIMA(30, 1, 1)   Log Likelihood                3464.199
Date:                Sun, 24 Dec 2023   AIC                          -6864.398
Time:                        22:07:13   BIC                          -6615.056
Sample:                             0   HQIC                         -6782.378
                              - 17890                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1915      0.005   -251.046      0.000      -1.201      -1.182
ar.L2         -0.1777      0.004    -40.023      0.000      -0.186      -0.169
ar.L3          0.0014      0.006      0.246      0.806      -0.010       0.012
ar.L4         -0.0511      0.006     -8.036      0.000      -0.064      -0.039
ar.L5         -0.0009      0.006     -0.156      0.876      -0.013       0.011
ar.L6         -0.0113      0.006     -1.854      0.064      -0.023       0.001
ar.L7         -0.0214      0.006     -3.575      0.000      -0.033      -0.010
ar.L8         -0.0170      0.006     -2.680      0.007      -0.029      -0.005
ar.L9          0.0097      0.006      1.528      0.127      -0.003       0.022
ar.L10         0.0487      0.007      7.008      0.000       0.035       0.062
ar.L11         0.0482      0.007      6.993      0.000       0.035       0.062
ar.L12         0.0209      0.007      2.969      0.003       0.007       0.035
ar.L13         0.0338      0.007      4.894      0.000       0.020       0.047
ar.L14         0.0251      0.007      3.787      0.000       0.012       0.038
ar.L15         0.0120      0.007      1.798      0.072      -0.001       0.025
ar.L16        -0.0520      0.006     -8.304      0.000      -0.064      -0.040
ar.L17        -0.0526      0.006     -9.031      0.000      -0.064      -0.041
ar.L18         0.1165      0.005     22.669      0.000       0.106       0.127
ar.L19         0.2086      0.004     46.383      0.000       0.200       0.217
ar.L20        -0.0780      0.005    -14.401      0.000      -0.089      -0.067
ar.L21        -0.1929      0.006    -34.172      0.000      -0.204      -0.182
ar.L22        -0.0668      0.007     -9.565      0.000      -0.081      -0.053
ar.L23        -0.0542      0.006     -8.348      0.000      -0.067      -0.041
ar.L24        -0.0163      0.006     -2.583      0.010      -0.029      -0.004
ar.L25         0.0088      0.006      1.425      0.154      -0.003       0.021
ar.L26         0.0298      0.007      4.457      0.000       0.017       0.043
ar.L27         0.0037      0.007      0.507      0.612      -0.011       0.018
ar.L28        -0.0293      0.008     -3.839      0.000      -0.044      -0.014
ar.L29        -0.0043      0.007     -0.597      0.551      -0.018       0.010
ar.L30         0.0590      0.005     11.441      0.000       0.049       0.069
ma.L1          0.8745      0.004    227.320      0.000       0.867       0.882
sigma2         0.0397      0.000    289.283      0.000       0.039       0.040
===================================================================================
Ljung-Box (L1) (Q):                   1.43   Jarque-Bera (JB):            547526.66
Prob(Q):                              0.23   Prob(JB):                         0.00
Heteroskedasticity (H):              69.39   Skew:                             0.81
Prob(H) (two-sided):                  0.00   Kurtosis:                        30.05
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890    54.650096
17891    54.566456
17892    54.497238
17893    54.515978
17894    54.450404
17895    54.515582
17896    54.512946
17897    54.673612
17898    54.602040
17899    54.591857
Name: predicted_mean, dtype: float64
       lower price  upper price
17890    54.259510    55.040681
17891    54.093465    55.039448
17892    53.911846    55.082631
17893    53.869492    55.162464
17894    53.727082    55.173726
17895    53.738742    55.292422
17896    53.674661    55.351230
17897    53.789416    55.557807
17898    53.665582    55.538499
17899    53.611789    55.571925

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.7865079316180594
Weighted Mean Absolute Percentage Error (WMAPE): 1.3513770747596507
In [741]:
forecastplusyahoo('TAVHL', 30, 1, 3)

                            price short_name
timestamp                                   
2021-12-27 09:00:00+03:00   33.24      TAVHL
2021-12-27 10:00:00+03:00   33.68      TAVHL
2021-12-27 11:00:00+03:00   33.36      TAVHL
2021-12-27 12:00:00+03:00   33.20      TAVHL
2021-12-27 13:00:00+03:00   33.20      TAVHL
...                           ...        ...
2023-09-22 14:00:00+03:00  118.70      TAVHL
2023-09-22 15:00:00+03:00  118.70      TAVHL
2023-09-22 16:00:00+03:00  118.80      TAVHL
2023-09-22 17:00:00+03:00  119.20      TAVHL
2023-09-22 18:00:00+03:00  119.20      TAVHL

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for TAVHL.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                               price
2021-12-27 06:00:00+00:00  33.240000
2021-12-27 06:30:00+00:00  33.220001
2021-12-27 07:00:00+00:00  33.680000
2021-12-27 07:30:00+00:00  33.340000
2021-12-27 08:00:00+00:00  33.360000
2021-12-27 08:30:00+00:00  33.360001
2021-12-27 09:00:00+00:00  33.200000
2021-12-27 09:30:00+00:00  33.020000
2021-12-27 10:00:00+00:00  33.200000
2021-12-27 10:30:00+00:00  33.099998
2021-12-27 11:00:00+00:00  32.640000
2021-12-27 11:30:00+00:00  32.560001
2021-12-27 12:00:00+00:00  32.760000
2021-12-27 12:30:00+00:00  32.759998
2021-12-27 13:00:00+00:00  32.780000
2021-12-27 13:30:00+00:00  33.000000
2021-12-27 14:00:00+00:00  32.600000
2021-12-27 15:00:00+00:00  32.500000
2021-12-28 06:00:00+00:00  32.600000
2021-12-28 06:30:00+00:00  32.680000
2021-12-28 07:00:00+00:00  32.900000
2021-12-28 07:30:00+00:00  32.900002
2021-12-28 08:00:00+00:00  32.700000
2021-12-28 08:30:00+00:00  32.660000
2021-12-28 09:00:00+00:00  32.560000
2021-12-28 09:30:00+00:00  32.580002
2021-12-28 10:00:00+00:00  32.420000
2021-12-28 10:30:00+00:00  32.259998
2021-12-28 11:00:00+00:00  32.180000
2021-12-28 11:30:00+00:00  32.459999
2021-12-28 12:00:00+00:00  32.300000
2021-12-28 12:30:00+00:00  32.119999
2021-12-28 13:00:00+00:00  31.500000
2021-12-28 13:30:00+00:00  31.100000
2021-12-28 14:00:00+00:00  31.540000
2021-12-28 15:00:00+00:00  31.560000
2021-12-29 06:00:00+00:00  31.120000
2021-12-29 06:30:00+00:00  30.959999
2021-12-29 07:00:00+00:00  31.220000
2021-12-29 07:30:00+00:00  31.360001
2021-12-29 08:00:00+00:00  31.560000
2021-12-29 08:30:00+00:00  31.459999
2021-12-29 09:00:00+00:00  31.780000
2021-12-29 09:30:00+00:00  31.719999
2021-12-29 10:00:00+00:00  31.780000
2021-12-29 10:30:00+00:00  31.639999
2021-12-29 11:00:00+00:00  32.140000
2021-12-29 11:30:00+00:00  32.299999
2021-12-29 12:00:00+00:00  32.500000
2021-12-29 12:30:00+00:00  32.560001
                                price
2023-09-21 10:00:00+00:00  117.700000
2023-09-21 10:30:00+00:00  118.900002
2023-09-21 11:00:00+00:00  121.900000
2023-09-21 11:30:00+00:00  121.000000
2023-09-21 12:00:00+00:00  120.600000
2023-09-21 12:30:00+00:00  121.400002
2023-09-21 13:00:00+00:00  120.700000
2023-09-21 13:30:00+00:00  121.199997
2023-09-21 14:00:00+00:00  121.900000
2023-09-21 14:30:00+00:00  121.900002
2023-09-21 15:00:00+00:00  122.900000
2023-09-22 06:00:00+00:00  119.500000
2023-09-22 06:30:00+00:00  119.900002
2023-09-22 07:00:00+00:00  119.000000
2023-09-22 07:30:00+00:00  119.599998
2023-09-22 08:00:00+00:00  119.300000
2023-09-22 08:30:00+00:00  118.900002
2023-09-22 09:00:00+00:00  119.200000
2023-09-22 09:30:00+00:00  119.400002
2023-09-22 10:00:00+00:00  119.400000
2023-09-22 10:30:00+00:00  119.199997
2023-09-22 11:00:00+00:00  118.700000
2023-09-22 11:30:00+00:00  118.199997
2023-09-22 12:00:00+00:00  118.700000
2023-09-22 12:30:00+00:00  119.099998
2023-09-22 13:00:00+00:00  118.800000
2023-09-22 13:30:00+00:00  118.699997
2023-09-22 14:00:00+00:00  119.200000
2023-09-22 14:30:00+00:00  119.199997
2023-09-22 15:00:00+00:00  119.200000
Mean of the first 10 values: price    121.53
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  119.3
2023-09-25 10:00:00+03:00  118.9
2023-09-25 11:00:00+03:00  121.1
2023-09-25 12:00:00+03:00  121.1
2023-09-25 13:00:00+03:00  122.7
2023-09-25 14:00:00+03:00  121.9
2023-09-25 15:00:00+03:00  121.8
2023-09-25 16:00:00+03:00  121.9
2023-09-25 17:00:00+03:00  123.5
2023-09-25 18:00:00+03:00  123.1
                              price
2018-01-02 10:00:00+03:00  0.163500
2018-01-02 11:00:00+03:00 -0.065400
2018-01-02 12:00:00+03:00  0.049000
2018-01-02 13:00:00+03:00  0.000000
2018-01-02 14:00:00+03:00 -0.016400
...                             ...
2023-09-22 13:00:00+00:00 -0.299998
2023-09-22 13:30:00+00:00 -0.100003
2023-09-22 14:00:00+00:00  0.500003
2023-09-22 14:30:00+00:00 -0.000003
2023-09-22 15:00:00+00:00  0.000003

[17889 rows x 1 columns]
ADF Statistic: -28.682964137099823
p-value: 0.0
Critical Values: {'1%': -3.4307160308760696, '5%': -2.8617017717195736, '10%': -2.566856106850706}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17890
Model:                ARIMA(30, 3, 1)   Log Likelihood               -8407.569
Date:                Sun, 24 Dec 2023   AIC                          16879.137
Time:                        22:20:36   BIC                          17128.476
Sample:                             0   HQIC                         16961.157
                              - 17890                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.9868      0.003   -305.198      0.000      -0.993      -0.980
ar.L2         -0.9750      0.005   -191.263      0.000      -0.985      -0.965
ar.L3         -0.9487      0.007   -131.083      0.000      -0.963      -0.935
ar.L4         -0.9108      0.008   -108.663      0.000      -0.927      -0.894
ar.L5         -0.8752      0.010    -90.847      0.000      -0.894      -0.856
ar.L6         -0.8296      0.011    -77.418      0.000      -0.851      -0.809
ar.L7         -0.7967      0.012    -67.791      0.000      -0.820      -0.774
ar.L8         -0.7680      0.012    -61.513      0.000      -0.793      -0.744
ar.L9         -0.7328      0.013    -56.767      0.000      -0.758      -0.707
ar.L10        -0.6941      0.014    -51.380      0.000      -0.721      -0.668
ar.L11        -0.6454      0.014    -45.496      0.000      -0.673      -0.618
ar.L12        -0.6300      0.014    -43.845      0.000      -0.658      -0.602
ar.L13        -0.5922      0.015    -40.208      0.000      -0.621      -0.563
ar.L14        -0.5422      0.015    -36.538      0.000      -0.571      -0.513
ar.L15        -0.5114      0.015    -33.597      0.000      -0.541      -0.482
ar.L16        -0.4605      0.015    -30.455      0.000      -0.490      -0.431
ar.L17        -0.4235      0.015    -28.651      0.000      -0.452      -0.395
ar.L18        -0.3818      0.015    -25.961      0.000      -0.411      -0.353
ar.L19        -0.3340      0.014    -23.188      0.000      -0.362      -0.306
ar.L20        -0.3361      0.014    -23.849      0.000      -0.364      -0.309
ar.L21        -0.3306      0.014    -23.778      0.000      -0.358      -0.303
ar.L22        -0.2992      0.013    -22.542      0.000      -0.325      -0.273
ar.L23        -0.2666      0.013    -20.656      0.000      -0.292      -0.241
ar.L24        -0.2367      0.012    -19.060      0.000      -0.261      -0.212
ar.L25        -0.1940      0.012    -16.739      0.000      -0.217      -0.171
ar.L26        -0.1492      0.011    -13.889      0.000      -0.170      -0.128
ar.L27        -0.1354      0.010    -13.991      0.000      -0.154      -0.116
ar.L28        -0.0971      0.008    -11.768      0.000      -0.113      -0.081
ar.L29        -0.0583      0.007     -8.206      0.000      -0.072      -0.044
ar.L30        -0.0397      0.005     -8.115      0.000      -0.049      -0.030
ma.L1         -1.0000      0.004   -234.226      0.000      -1.008      -0.992
sigma2         0.1498      0.001    215.525      0.000       0.148       0.151
===================================================================================
Ljung-Box (L1) (Q):                   0.05   Jarque-Bera (JB):            510575.05
Prob(Q):                              0.82   Prob(JB):                         0.00
Heteroskedasticity (H):               8.78   Skew:                             0.41
Prob(H) (two-sided):                  0.00   Kurtosis:                        29.16
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890    119.211894
17891    119.376121
17892    119.420773
17893    119.541441
17894    119.469429
17895    119.385217
17896    119.355988
17897    119.245598
17898    119.246209
17899    119.143750
Name: predicted_mean, dtype: float64
       lower price  upper price
17890   118.453196   119.970592
17891   118.296015   120.456227
17892   118.089695   120.751851
17893   117.989428   121.093455
17894   117.710551   121.228307
17895   117.429150   121.341285
17896   117.205929   121.506047
17897   116.906713   121.584484
17898   116.723045   121.769373
17899   116.437451   121.850049

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 2.6269712520375847
Weighted Mean Absolute Percentage Error (WMAPE): 1.886995552488141
In [738]:
forecastplusyahoo('TKFEN', 30, 1, 1)

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00  19.0042      TKFEN
2021-12-27 10:00:00+03:00  19.1636      TKFEN
2021-12-27 11:00:00+03:00  18.8271      TKFEN
2021-12-27 12:00:00+03:00  18.7563      TKFEN
2021-12-27 13:00:00+03:00  18.7563      TKFEN
...                            ...        ...
2023-09-22 14:00:00+03:00  52.3000      TKFEN
2023-09-22 15:00:00+03:00  52.2000      TKFEN
2023-09-22 16:00:00+03:00  51.8000      TKFEN
2023-09-22 17:00:00+03:00  52.0000      TKFEN
2023-09-22 18:00:00+03:00  52.0000      TKFEN

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for TKFEN.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                               price
2021-12-27 06:00:00+00:00  19.004200
2021-12-27 06:30:00+00:00  21.580000
2021-12-27 07:00:00+00:00  19.163600
2021-12-27 07:30:00+00:00  21.360001
2021-12-27 08:00:00+00:00  18.827100
2021-12-27 08:30:00+00:00  21.280001
2021-12-27 09:00:00+00:00  18.756300
2021-12-27 09:30:00+00:00  21.120001
2021-12-27 10:00:00+00:00  18.756300
2021-12-27 10:30:00+00:00  21.160000
2021-12-27 11:00:00+00:00  18.579100
2021-12-27 11:30:00+00:00  20.900000
2021-12-27 12:00:00+00:00  18.614600
2021-12-27 12:30:00+00:00  20.879999
2021-12-27 13:00:00+00:00  18.579100
2021-12-27 13:30:00+00:00  21.059999
2021-12-27 14:00:00+00:00  18.508200
2021-12-27 15:00:00+00:00  18.437400
2021-12-28 06:00:00+00:00  18.596800
2021-12-28 06:30:00+00:00  20.780001
2021-12-28 07:00:00+00:00  18.490600
2021-12-28 07:30:00+00:00  20.840000
2021-12-28 08:00:00+00:00  18.402000
2021-12-28 08:30:00+00:00  20.620001
2021-12-28 09:00:00+00:00  18.278100
2021-12-28 09:30:00+00:00  20.580000
2021-12-28 10:00:00+00:00  18.047800
2021-12-28 10:30:00+00:00  20.400000
2021-12-28 11:00:00+00:00  17.959200
2021-12-28 11:30:00+00:00  20.400000
2021-12-28 12:00:00+00:00  18.136400
2021-12-28 12:30:00+00:00  20.340000
2021-12-28 13:00:00+00:00  17.906000
2021-12-28 13:30:00+00:00  19.799999
2021-12-28 14:00:00+00:00  17.711300
2021-12-28 15:00:00+00:00  17.658100
2021-12-29 06:00:00+00:00  17.569600
2021-12-29 06:30:00+00:00  19.719999
2021-12-29 07:00:00+00:00  17.498700
2021-12-29 07:30:00+00:00  20.139999
2021-12-29 08:00:00+00:00  17.941500
2021-12-29 08:30:00+00:00  20.180000
2021-12-29 09:00:00+00:00  17.976900
2021-12-29 09:30:00+00:00  20.280001
2021-12-29 10:00:00+00:00  18.171800
2021-12-29 10:30:00+00:00  20.280001
2021-12-29 11:00:00+00:00  18.100900
2021-12-29 11:30:00+00:00  20.600000
2021-12-29 12:00:00+00:00  18.189500
2021-12-29 12:30:00+00:00  20.780001
                               price
2023-09-21 10:00:00+00:00  47.880000
2023-09-21 10:30:00+00:00  48.459999
2023-09-21 11:00:00+00:00  48.920000
2023-09-21 11:30:00+00:00  49.220001
2023-09-21 12:00:00+00:00  49.080000
2023-09-21 12:30:00+00:00  49.380001
2023-09-21 13:00:00+00:00  50.450000
2023-09-21 13:30:00+00:00  50.950001
2023-09-21 14:00:00+00:00  50.950000
2023-09-21 14:30:00+00:00  50.950001
2023-09-21 15:00:00+00:00  50.950000
2023-09-22 06:00:00+00:00  53.800000
2023-09-22 06:30:00+00:00  52.849998
2023-09-22 07:00:00+00:00  52.550000
2023-09-22 07:30:00+00:00  52.750000
2023-09-22 08:00:00+00:00  52.350000
2023-09-22 08:30:00+00:00  52.450001
2023-09-22 09:00:00+00:00  52.300000
2023-09-22 09:30:00+00:00  52.200001
2023-09-22 10:00:00+00:00  52.550000
2023-09-22 10:30:00+00:00  52.349998
2023-09-22 11:00:00+00:00  52.300000
2023-09-22 11:30:00+00:00  51.849998
2023-09-22 12:00:00+00:00  52.200000
2023-09-22 12:30:00+00:00  52.049999
2023-09-22 13:00:00+00:00  51.800000
2023-09-22 13:30:00+00:00  51.950001
2023-09-22 14:00:00+00:00  52.000000
2023-09-22 14:30:00+00:00  52.000000
2023-09-22 15:00:00+00:00  52.000000
Mean of the first 10 values: price    52.09
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  52.20
2023-09-25 10:00:00+03:00  52.05
2023-09-25 11:00:00+03:00  51.90
2023-09-25 12:00:00+03:00  52.30
2023-09-25 13:00:00+03:00  52.25
2023-09-25 14:00:00+03:00  51.85
2023-09-25 15:00:00+03:00  52.00
2023-09-25 16:00:00+03:00  52.00
2023-09-25 17:00:00+03:00  52.05
2023-09-25 18:00:00+03:00  52.30
                              price
2018-01-02 10:00:00+03:00  0.060000
2018-01-02 11:00:00+03:00 -0.097400
2018-01-02 12:00:00+03:00 -0.112300
2018-01-02 13:00:00+03:00  0.000000
2018-01-02 14:00:00+03:00  0.007500
...                             ...
2023-09-22 13:00:00+00:00 -0.249999
2023-09-22 13:30:00+00:00  0.150001
2023-09-22 14:00:00+00:00  0.049999
2023-09-22 14:30:00+00:00  0.000000
2023-09-22 15:00:00+00:00  0.000000

[17890 rows x 1 columns]
ADF Statistic: -20.287454739221047
p-value: 0.0
Critical Values: {'1%': -3.4307165027125293, '5%': -2.861701980240464, '10%': -2.5668562178431515}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17891
Model:                ARIMA(30, 1, 1)   Log Likelihood              -11183.700
Date:                Sun, 24 Dec 2023   AIC                          22431.399
Time:                        22:10:23   BIC                          22680.743
Sample:                             0   HQIC                         22513.419
                              - 17891                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.4903      0.276     -1.775      0.076      -1.032       0.051
ar.L2          0.2500      0.257      0.974      0.330      -0.253       0.753
ar.L3          0.0211      0.044      0.484      0.629      -0.064       0.107
ar.L4          0.0207      0.014      1.442      0.149      -0.007       0.049
ar.L5         -0.0563      0.006     -8.722      0.000      -0.069      -0.044
ar.L6          0.0128      0.017      0.742      0.458      -0.021       0.046
ar.L7         -0.0299      0.008     -3.809      0.000      -0.045      -0.015
ar.L8          0.0181      0.013      1.428      0.153      -0.007       0.043
ar.L9         -0.0309      0.008     -4.096      0.000      -0.046      -0.016
ar.L10         0.0431      0.011      3.957      0.000       0.022       0.064
ar.L11        -0.0007      0.010     -0.069      0.945      -0.020       0.019
ar.L12        -0.0306      0.008     -3.802      0.000      -0.046      -0.015
ar.L13        -0.0204      0.008     -2.612      0.009      -0.036      -0.005
ar.L14        -0.0435      0.010     -4.579      0.000      -0.062      -0.025
ar.L15        -0.0193      0.017     -1.145      0.252      -0.052       0.014
ar.L16        -0.1756      0.013    -13.652      0.000      -0.201      -0.150
ar.L17        -0.0292      0.054     -0.540      0.589      -0.135       0.077
ar.L18         0.4408      0.032     13.740      0.000       0.378       0.504
ar.L19         0.3749      0.108      3.481      0.000       0.164       0.586
ar.L20        -0.2882      0.151     -1.913      0.056      -0.584       0.007
ar.L21        -0.1780      0.014    -12.283      0.000      -0.206      -0.150
ar.L22        -0.0015      0.056     -0.026      0.979      -0.110       0.107
ar.L23        -0.0386      0.025     -1.532      0.125      -0.088       0.011
ar.L24         0.0227      0.022      1.025      0.305      -0.021       0.066
ar.L25        -0.0385      0.008     -4.929      0.000      -0.054      -0.023
ar.L26         0.0255      0.014      1.851      0.064      -0.002       0.053
ar.L27        -0.0322      0.008     -4.076      0.000      -0.048      -0.017
ar.L28         0.0010      0.012      0.083      0.934      -0.022       0.024
ar.L29        -0.0614      0.007     -8.556      0.000      -0.075      -0.047
ar.L30         0.0519      0.020      2.540      0.011       0.012       0.092
ma.L1         -0.4376      0.276     -1.585      0.113      -0.979       0.103
sigma2         0.2040      0.001    206.976      0.000       0.202       0.206
===================================================================================
Ljung-Box (L1) (Q):                   0.14   Jarque-Bera (JB):            123759.96
Prob(Q):                              0.71   Prob(JB):                         0.00
Heteroskedasticity (H):              12.60   Skew:                            -1.00
Prob(H) (two-sided):                  0.00   Kurtosis:                        15.73
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891    53.083374
17892    51.299178
17893    52.164979
17894    51.497429
17895    51.807338
17896    51.443712
17897    51.712360
17898    51.662947
17899    51.969321
17900    51.473314
Name: predicted_mean, dtype: float64
       lower price  upper price
17891    52.198086    53.968662
17892    50.411594    52.186762
17893    51.042026    53.287932
17894    50.357637    52.637222
17895    50.522138    53.092538
17896    50.139571    52.747853
17897    50.291568    53.133152
17898    50.223832    53.102061
17899    50.426741    53.511902
17900    49.914911    53.031718

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.5749518552037074
Weighted Mean Absolute Percentage Error (WMAPE): 0.9672679786837399
In [739]:
forecastplusyahoo('TUPRS',30,1,3)

                              price short_name
timestamp                                     
2021-12-27 09:00:00+03:00   19.6507      TUPRS
2021-12-27 10:00:00+03:00   20.4990      TUPRS
2021-12-27 11:00:00+03:00   20.2331      TUPRS
2021-12-27 12:00:00+03:00   19.9926      TUPRS
2021-12-27 13:00:00+03:00   20.0052      TUPRS
...                             ...        ...
2023-09-22 14:00:00+03:00  145.8839      TUPRS
2023-09-22 15:00:00+03:00  147.0288      TUPRS
2023-09-22 16:00:00+03:00  147.8875      TUPRS
2023-09-22 17:00:00+03:00  148.0784      TUPRS
2023-09-22 18:00:00+03:00  147.0288      TUPRS

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for TUPRS.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                               price
2021-12-27 06:00:00+00:00  19.650700
2021-12-27 06:30:00+00:00  22.614286
2021-12-27 07:00:00+00:00  20.499000
2021-12-27 07:30:00+00:00  22.842855
2021-12-27 08:00:00+00:00  20.233100
2021-12-27 08:30:00+00:00  22.685715
2021-12-27 09:00:00+00:00  19.992600
2021-12-27 09:30:00+00:00  22.414284
2021-12-27 10:00:00+00:00  20.005200
2021-12-27 10:30:00+00:00  22.471428
2021-12-27 11:00:00+00:00  19.714000
2021-12-27 11:30:00+00:00  22.171429
2021-12-27 12:00:00+00:00  19.688700
2021-12-27 12:30:00+00:00  22.114286
2021-12-27 13:00:00+00:00  19.600100
2021-12-27 13:30:00+00:00  22.199999
2021-12-27 14:00:00+00:00  19.346800
2021-12-27 15:00:00+00:00  19.346800
2021-12-28 06:00:00+00:00  19.752000
2021-12-28 06:30:00+00:00  22.057142
2021-12-28 07:00:00+00:00  19.777300
2021-12-28 07:30:00+00:00  22.057142
2021-12-28 08:00:00+00:00  19.574700
2021-12-28 08:30:00+00:00  22.171429
2021-12-28 09:00:00+00:00  19.587400
2021-12-28 09:30:00+00:00  22.142857
2021-12-28 10:00:00+00:00  19.524100
2021-12-28 10:30:00+00:00  22.100000
2021-12-28 11:00:00+00:00  19.422800
2021-12-28 11:30:00+00:00  21.914284
2021-12-28 12:00:00+00:00  19.384800
2021-12-28 12:30:00+00:00  21.814285
2021-12-28 13:00:00+00:00  19.207500
2021-12-28 13:30:00+00:00  21.228573
2021-12-28 14:00:00+00:00  18.928900
2021-12-28 15:00:00+00:00  18.941600
2021-12-29 06:00:00+00:00  18.941600
2021-12-29 06:30:00+00:00  21.199999
2021-12-29 07:00:00+00:00  18.865700
2021-12-29 07:30:00+00:00  21.614286
2021-12-29 08:00:00+00:00  19.220200
2021-12-29 08:30:00+00:00  21.828571
2021-12-29 09:00:00+00:00  19.384800
2021-12-29 09:30:00+00:00  22.042856
2021-12-29 10:00:00+00:00  19.574700
2021-12-29 10:30:00+00:00  21.957144
2021-12-29 11:00:00+00:00  19.612700
2021-12-29 11:30:00+00:00  22.257143
2021-12-29 12:00:00+00:00  19.650700
2021-12-29 12:30:00+00:00  22.342855
                                price
2023-09-21 10:00:00+00:00  134.530000
2023-09-21 10:30:00+00:00  141.600006
2023-09-21 11:00:00+00:00  137.583100
2023-09-21 11:30:00+00:00  144.699997
2023-09-21 12:00:00+00:00  138.155600
2023-09-21 12:30:00+00:00  146.600006
2023-09-21 13:00:00+00:00  139.586800
2023-09-21 13:30:00+00:00  147.100006
2023-09-21 14:00:00+00:00  141.017900
2023-09-21 14:30:00+00:00  147.800003
2023-09-21 15:00:00+00:00  141.017900
2023-09-22 06:00:00+00:00  141.208700
2023-09-22 06:30:00+00:00  154.600006
2023-09-22 07:00:00+00:00  145.788500
2023-09-22 07:30:00+00:00  152.899994
2023-09-22 08:00:00+00:00  146.361000
2023-09-22 08:30:00+00:00  153.500000
2023-09-22 09:00:00+00:00  147.410500
2023-09-22 09:30:00+00:00  153.899994
2023-09-22 10:00:00+00:00  146.647200
2023-09-22 10:30:00+00:00  153.000000
2023-09-22 11:00:00+00:00  145.883900
2023-09-22 11:30:00+00:00  152.899994
2023-09-22 12:00:00+00:00  147.028800
2023-09-22 12:30:00+00:00  154.699997
2023-09-22 13:00:00+00:00  147.887500
2023-09-22 13:30:00+00:00  154.500000
2023-09-22 14:00:00+00:00  148.078400
2023-09-22 14:30:00+00:00  155.199997
2023-09-22 15:00:00+00:00  147.028800
Mean of the first 10 values: price    155.96888
dtype: float64
                              price
timestamp                          
2023-09-25 09:00:00+03:00  150.0820
2023-09-25 10:00:00+03:00  153.3260
2023-09-25 11:00:00+03:00  154.8526
2023-09-25 12:00:00+03:00  157.2379
2023-09-25 13:00:00+03:00  156.2837
2023-09-25 14:00:00+03:00  155.9975
2023-09-25 15:00:00+03:00  157.8103
2023-09-25 16:00:00+03:00  156.8562
2023-09-25 17:00:00+03:00  158.8598
2023-09-25 18:00:00+03:00  158.3828
                              price
2018-01-02 10:00:00+03:00  0.070300
2018-01-02 11:00:00+03:00 -0.020100
2018-01-02 12:00:00+03:00  0.010100
2018-01-02 13:00:00+03:00  0.020000
2018-01-02 14:00:00+03:00  0.030200
...                             ...
2023-09-22 13:00:00+00:00 -6.812497
2023-09-22 13:30:00+00:00  6.612500
2023-09-22 14:00:00+00:00 -6.421600
2023-09-22 14:30:00+00:00  7.121597
2023-09-22 15:00:00+00:00 -8.171197

[17890 rows x 1 columns]
ADF Statistic: -19.38840832103753
p-value: 0.0
Critical Values: {'1%': -3.4307165027125293, '5%': -2.861701980240464, '10%': -2.5668562178431515}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17891
Model:                ARIMA(30, 3, 1)   Log Likelihood              -24997.129
Date:                Sun, 24 Dec 2023   AIC                          50058.258
Time:                        22:13:30   BIC                          50307.599
Sample:                             0   HQIC                         50140.278
                              - 17891                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.9439      0.011   -184.448      0.000      -1.965      -1.923
ar.L2         -2.0630      0.030    -69.270      0.000      -2.121      -2.005
ar.L3         -1.9327      0.049    -39.638      0.000      -2.028      -1.837
ar.L4         -1.6895      0.065    -25.898      0.000      -1.817      -1.562
ar.L5         -1.4618      0.079    -18.566      0.000      -1.616      -1.307
ar.L6         -1.1725      0.090    -13.076      0.000      -1.348      -0.997
ar.L7         -0.9419      0.097     -9.672      0.000      -1.133      -0.751
ar.L8         -0.7507      0.103     -7.308      0.000      -0.952      -0.549
ar.L9         -0.6409      0.106     -6.036      0.000      -0.849      -0.433
ar.L10        -0.4753      0.109     -4.366      0.000      -0.689      -0.262
ar.L11        -0.2882      0.109     -2.633      0.008      -0.503      -0.074
ar.L12        -0.0651      0.108     -0.601      0.548      -0.277       0.147
ar.L13         0.0991      0.106      0.934      0.350      -0.109       0.307
ar.L14         0.1707      0.103      1.661      0.097      -0.031       0.372
ar.L15         0.1959      0.099      1.981      0.048       0.002       0.390
ar.L16         0.0956      0.095      1.005      0.315      -0.091       0.282
ar.L17         0.1754      0.093      1.890      0.059      -0.007       0.357
ar.L18         0.7820      0.090      8.714      0.000       0.606       0.958
ar.L19         1.6629      0.081     20.557      0.000       1.504       1.821
ar.L20         1.8672      0.065     28.877      0.000       1.740       1.994
ar.L21         1.6554      0.049     34.024      0.000       1.560       1.751
ar.L22         1.4055      0.037     38.034      0.000       1.333       1.478
ar.L23         1.1273      0.030     37.215      0.000       1.068       1.187
ar.L24         0.9067      0.027     33.486      0.000       0.854       0.960
ar.L25         0.6717      0.025     26.830      0.000       0.623       0.721
ar.L26         0.5586      0.023     24.786      0.000       0.514       0.603
ar.L27         0.4730      0.020     23.884      0.000       0.434       0.512
ar.L28         0.4042      0.017     23.856      0.000       0.371       0.437
ar.L29         0.2577      0.013     20.109      0.000       0.233       0.283
ar.L30         0.1201      0.006     20.260      0.000       0.109       0.132
ma.L1         -0.9423      0.009   -102.775      0.000      -0.960      -0.924
sigma2         0.9511      0.005    210.597      0.000       0.942       0.960
===================================================================================
Ljung-Box (L1) (Q):                  29.99   Jarque-Bera (JB):            220509.09
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):              78.74   Skew:                            -0.83
Prob(H) (two-sided):                  0.00   Kurtosis:                        20.12
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891    157.133317
17892    156.276642
17893    152.934216
17894    155.769074
17895    152.244440
17896    156.116036
17897    151.898937
17898    154.619788
17899    150.415592
17900    153.223418
Name: predicted_mean, dtype: float64
       lower price  upper price
17891   155.221892   159.044743
17892   154.352869   158.200415
17893   150.367638   155.500794
17894   153.061658   158.476489
17895   148.976546   155.512335
17896   152.583161   159.648911
17897   147.728434   156.069440
17898   150.089144   159.150433
17899   145.200584   155.630600
17900   147.571094   158.875742

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 4.674016512569116
Weighted Mean Absolute Percentage Error (WMAPE): 2.0122977706869536
In [745]:
forecastplusyahoo('TTKOM', 30, 1, 3)

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00   9.3416      TTKOM
2021-12-27 10:00:00+03:00   9.3416      TTKOM
2021-12-27 11:00:00+03:00   9.3061      TTKOM
2021-12-27 12:00:00+03:00   9.2795      TTKOM
2021-12-27 13:00:00+03:00   9.3061      TTKOM
...                            ...        ...
2023-09-22 14:00:00+03:00  23.2200      TTKOM
2023-09-22 15:00:00+03:00  23.1800      TTKOM
2023-09-22 16:00:00+03:00  23.2200      TTKOM
2023-09-22 17:00:00+03:00  23.2000      TTKOM
2023-09-22 18:00:00+03:00  23.1800      TTKOM

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for TTKOM.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                             price
2021-12-27 06:00:00+00:00   9.3416
2021-12-27 06:30:00+00:00  10.5200
2021-12-27 07:00:00+00:00   9.3416
2021-12-27 07:30:00+00:00  10.5100
2021-12-27 08:00:00+00:00   9.3061
2021-12-27 08:30:00+00:00  10.5100
2021-12-27 09:00:00+00:00   9.2795
2021-12-27 09:30:00+00:00  10.4400
2021-12-27 10:00:00+00:00   9.3061
2021-12-27 10:30:00+00:00  10.4900
2021-12-27 11:00:00+00:00   9.1732
2021-12-27 11:30:00+00:00  10.2800
2021-12-27 12:00:00+00:00   9.0934
2021-12-27 12:30:00+00:00  10.3100
2021-12-27 13:00:00+00:00   9.1554
2021-12-27 13:30:00+00:00  10.3500
2021-12-27 14:00:00+00:00   9.1377
2021-12-27 15:00:00+00:00   9.1200
2021-12-28 06:00:00+00:00   9.1023
2021-12-28 06:30:00+00:00  10.3700
2021-12-28 07:00:00+00:00   9.2086
2021-12-28 07:30:00+00:00  10.4100
2021-12-28 08:00:00+00:00   9.1998
2021-12-28 08:30:00+00:00  10.3500
2021-12-28 09:00:00+00:00   9.1466
2021-12-28 09:30:00+00:00  10.3100
2021-12-28 10:00:00+00:00   9.1377
2021-12-28 10:30:00+00:00  10.2800
2021-12-28 11:00:00+00:00   9.0402
2021-12-28 11:30:00+00:00  10.2300
2021-12-28 12:00:00+00:00   9.0668
2021-12-28 12:30:00+00:00  10.2000
2021-12-28 13:00:00+00:00   8.9605
2021-12-28 13:30:00+00:00   9.8800
2021-12-28 14:00:00+00:00   8.7566
2021-12-28 15:00:00+00:00   8.7300
2021-12-29 06:00:00+00:00   8.7123
2021-12-29 06:30:00+00:00   9.7900
2021-12-29 07:00:00+00:00   8.7212
2021-12-29 07:30:00+00:00   9.8800
2021-12-29 08:00:00+00:00   8.7832
2021-12-29 08:30:00+00:00   9.9600
2021-12-29 09:00:00+00:00   8.8364
2021-12-29 09:30:00+00:00   9.9200
2021-12-29 10:00:00+00:00   8.8187
2021-12-29 10:30:00+00:00   9.8700
2021-12-29 11:00:00+00:00   8.7655
2021-12-29 11:30:00+00:00   9.9500
2021-12-29 12:00:00+00:00   8.8541
2021-12-29 12:30:00+00:00  10.0500
                               price
2023-09-21 10:00:00+00:00  22.520000
2023-09-21 10:30:00+00:00  22.820000
2023-09-21 11:00:00+00:00  23.120000
2023-09-21 11:30:00+00:00  22.980000
2023-09-21 12:00:00+00:00  22.940000
2023-09-21 12:30:00+00:00  23.219999
2023-09-21 13:00:00+00:00  22.980000
2023-09-21 13:30:00+00:00  23.139999
2023-09-21 14:00:00+00:00  23.260000
2023-09-21 14:30:00+00:00  23.260000
2023-09-21 15:00:00+00:00  23.340000
2023-09-22 06:00:00+00:00  23.380000
2023-09-22 06:30:00+00:00  23.280001
2023-09-22 07:00:00+00:00  23.340000
2023-09-22 07:30:00+00:00  23.620001
2023-09-22 08:00:00+00:00  23.620000
2023-09-22 08:30:00+00:00  23.500000
2023-09-22 09:00:00+00:00  23.480000
2023-09-22 09:30:00+00:00  23.360001
2023-09-22 10:00:00+00:00  23.420000
2023-09-22 10:30:00+00:00  23.320000
2023-09-22 11:00:00+00:00  23.220000
2023-09-22 11:30:00+00:00  23.080000
2023-09-22 12:00:00+00:00  23.180000
2023-09-22 12:30:00+00:00  23.260000
2023-09-22 13:00:00+00:00  23.220000
2023-09-22 13:30:00+00:00  23.160000
2023-09-22 14:00:00+00:00  23.200000
2023-09-22 14:30:00+00:00  23.200001
2023-09-22 15:00:00+00:00  23.180000
Mean of the first 10 values: price    23.638
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  23.40
2023-09-25 10:00:00+03:00  23.62
2023-09-25 11:00:00+03:00  23.52
2023-09-25 12:00:00+03:00  23.70
2023-09-25 13:00:00+03:00  23.70
2023-09-25 14:00:00+03:00  23.52
2023-09-25 15:00:00+03:00  23.48
2023-09-25 16:00:00+03:00  23.56
2023-09-25 17:00:00+03:00  23.94
2023-09-25 18:00:00+03:00  23.94
                                  price
2018-01-02 10:00:00+03:00  1.123000e-01
2018-01-02 11:00:00+03:00  0.000000e+00
2018-01-02 12:00:00+03:00 -1.610000e-02
2018-01-02 13:00:00+03:00  0.000000e+00
2018-01-02 14:00:00+03:00  2.410000e-02
...                                 ...
2023-09-22 13:00:00+00:00 -4.000023e-02
2023-09-22 13:30:00+00:00 -6.000015e-02
2023-09-22 14:00:00+00:00  4.000015e-02
2023-09-22 14:30:00+00:00  7.629395e-07
2023-09-22 15:00:00+00:00 -2.000076e-02

[17890 rows x 1 columns]
ADF Statistic: -19.370951544217785
p-value: 0.0
Critical Values: {'1%': -3.4307165027125293, '5%': -2.861701980240464, '10%': -2.5668562178431515}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17891
Model:                ARIMA(30, 3, 1)   Log Likelihood               11772.101
Date:                Sun, 24 Dec 2023   AIC                         -23480.202
Time:                        22:30:19   BIC                         -23230.862
Sample:                             0   HQIC                        -23398.183
                              - 17891                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.2435      0.004   -303.873      0.000      -1.252      -1.236
ar.L2         -1.2185      0.008   -146.330      0.000      -1.235      -1.202
ar.L3         -1.2852      0.012   -111.606      0.000      -1.308      -1.263
ar.L4         -1.2370      0.015    -83.193      0.000      -1.266      -1.208
ar.L5         -1.2729      0.018    -72.414      0.000      -1.307      -1.238
ar.L6         -1.2313      0.020    -61.552      0.000      -1.270      -1.192
ar.L7         -1.2545      0.022    -57.520      0.000      -1.297      -1.212
ar.L8         -1.2125      0.024    -51.462      0.000      -1.259      -1.166
ar.L9         -1.1984      0.025    -47.762      0.000      -1.248      -1.149
ar.L10        -1.1309      0.026    -42.914      0.000      -1.183      -1.079
ar.L11        -1.1024      0.027    -40.625      0.000      -1.156      -1.049
ar.L12        -1.0544      0.028    -38.155      0.000      -1.109      -1.000
ar.L13        -1.0281      0.028    -36.817      0.000      -1.083      -0.973
ar.L14        -0.9734      0.027    -35.461      0.000      -1.027      -0.920
ar.L15        -0.9387      0.027    -34.375      0.000      -0.992      -0.885
ar.L16        -0.9540      0.027    -35.167      0.000      -1.007      -0.901
ar.L17        -1.0646      0.027    -39.449      0.000      -1.117      -1.012
ar.L18        -0.5202      0.027    -19.117      0.000      -0.574      -0.467
ar.L19        -0.4939      0.026    -19.046      0.000      -0.545      -0.443
ar.L20        -0.5683      0.025    -23.167      0.000      -0.616      -0.520
ar.L21        -0.5275      0.023    -22.693      0.000      -0.573      -0.482
ar.L22        -0.5095      0.022    -23.332      0.000      -0.552      -0.467
ar.L23        -0.4232      0.021    -20.628      0.000      -0.463      -0.383
ar.L24        -0.3709      0.019    -19.676      0.000      -0.408      -0.334
ar.L25        -0.2738      0.017    -15.911      0.000      -0.308      -0.240
ar.L26        -0.2128      0.015    -13.756      0.000      -0.243      -0.182
ar.L27        -0.1560      0.013    -11.576      0.000      -0.182      -0.130
ar.L28        -0.1267      0.011    -11.590      0.000      -0.148      -0.105
ar.L29        -0.0822      0.009     -9.622      0.000      -0.099      -0.065
ar.L30        -0.0368      0.005     -7.279      0.000      -0.047      -0.027
ma.L1         -0.9355      0.003   -300.826      0.000      -0.942      -0.929
sigma2         0.0157    5.5e-05    285.916      0.000       0.016       0.016
===================================================================================
Ljung-Box (L1) (Q):                   0.18   Jarque-Bera (JB):            483315.68
Prob(Q):                              0.68   Prob(JB):                         0.00
Heteroskedasticity (H):              14.17   Skew:                             0.64
Prob(H) (two-sided):                  0.00   Kurtosis:                        28.43
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891    23.118978
17892    23.141193
17893    23.307552
17894    23.292809
17895    23.215727
17896    23.228411
17897    23.168260
17898    23.238292
17899    23.207234
17900    23.176528
Name: predicted_mean, dtype: float64
       lower price  upper price
17891    22.873296    23.364661
17892    22.823317    23.459068
17893    22.912594    23.702510
17894    22.838350    23.747269
17895    22.692848    23.738606
17896    22.647597    23.809224
17897    22.521299    23.815221
17898    22.532760    23.943823
17899    22.434982    23.979487
17900    22.341099    24.011956

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.46467470284892237
Weighted Mean Absolute Percentage Error (WMAPE): 1.812765818379819
In [743]:
forecastplusyahoo('TCELL', 30, 1, 1)

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00  18.8440      TCELL
2021-12-27 10:00:00+03:00  18.9601      TCELL
2021-12-27 11:00:00+03:00  18.7569      TCELL
2021-12-27 12:00:00+03:00  18.7182      TCELL
2021-12-27 13:00:00+03:00  18.7763      TCELL
...                            ...        ...
2023-09-22 14:00:00+03:00  54.4000      TCELL
2023-09-22 15:00:00+03:00  54.5000      TCELL
2023-09-22 16:00:00+03:00  54.5500      TCELL
2023-09-22 17:00:00+03:00  54.1000      TCELL
2023-09-22 18:00:00+03:00  54.4500      TCELL

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for TCELL.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                               price
2021-12-27 06:00:00+00:00  18.844000
2021-12-27 06:30:00+00:00  19.480000
2021-12-27 07:00:00+00:00  18.960100
2021-12-27 07:30:00+00:00  19.520000
2021-12-27 08:00:00+00:00  18.756900
2021-12-27 08:30:00+00:00  19.400000
2021-12-27 09:00:00+00:00  18.718200
2021-12-27 09:30:00+00:00  19.270000
2021-12-27 10:00:00+00:00  18.776300
2021-12-27 10:30:00+00:00  19.330000
2021-12-27 11:00:00+00:00  18.253600
2021-12-27 11:30:00+00:00  18.709999
2021-12-27 12:00:00+00:00  18.331000
2021-12-27 12:30:00+00:00  19.000000
2021-12-27 13:00:00+00:00  18.534300
2021-12-27 13:30:00+00:00  19.299999
2021-12-27 14:00:00+00:00  18.408500
2021-12-27 15:00:00+00:00  18.292300
2021-12-28 06:00:00+00:00  18.456900
2021-12-28 06:30:00+00:00  19.090000
2021-12-28 07:00:00+00:00  18.698800
2021-12-28 07:30:00+00:00  19.350000
2021-12-28 08:00:00+00:00  18.602000
2021-12-28 08:30:00+00:00  19.200001
2021-12-28 09:00:00+00:00  18.631100
2021-12-28 09:30:00+00:00  19.219999
2021-12-28 10:00:00+00:00  18.563300
2021-12-28 10:30:00+00:00  19.090000
2021-12-28 11:00:00+00:00  18.524600
2021-12-28 11:30:00+00:00  19.219999
2021-12-28 12:00:00+00:00  18.534300
2021-12-28 12:30:00+00:00  19.180000
2021-12-28 13:00:00+00:00  18.524600
2021-12-28 13:30:00+00:00  18.809999
2021-12-28 14:00:00+00:00  18.098800
2021-12-28 15:00:00+00:00  18.002000
2021-12-29 06:00:00+00:00  18.002000
2021-12-29 06:30:00+00:00  18.740000
2021-12-29 07:00:00+00:00  18.166500
2021-12-29 07:30:00+00:00  18.879999
2021-12-29 08:00:00+00:00  18.389100
2021-12-29 08:30:00+00:00  18.980000
2021-12-29 09:00:00+00:00  18.389100
2021-12-29 09:30:00+00:00  18.990000
2021-12-29 10:00:00+00:00  18.379400
2021-12-29 10:30:00+00:00  18.920000
2021-12-29 11:00:00+00:00  18.408500
2021-12-29 11:30:00+00:00  19.219999
2021-12-29 12:00:00+00:00  18.737500
2021-12-29 12:30:00+00:00  19.490000
                               price
2023-09-21 10:00:00+00:00  53.150000
2023-09-21 10:30:00+00:00  53.849998
2023-09-21 11:00:00+00:00  54.750000
2023-09-21 11:30:00+00:00  54.349998
2023-09-21 12:00:00+00:00  54.350000
2023-09-21 12:30:00+00:00  55.049999
2023-09-21 13:00:00+00:00  54.400000
2023-09-21 13:30:00+00:00  55.150002
2023-09-21 14:00:00+00:00  55.400000
2023-09-21 14:30:00+00:00  55.400002
2023-09-21 15:00:00+00:00  55.400000
2023-09-22 06:00:00+00:00  55.400000
2023-09-22 06:30:00+00:00  55.099998
2023-09-22 07:00:00+00:00  54.850000
2023-09-22 07:30:00+00:00  54.950001
2023-09-22 08:00:00+00:00  54.750000
2023-09-22 08:30:00+00:00  54.799999
2023-09-22 09:00:00+00:00  54.750000
2023-09-22 09:30:00+00:00  54.549999
2023-09-22 10:00:00+00:00  54.600000
2023-09-22 10:30:00+00:00  54.400002
2023-09-22 11:00:00+00:00  54.400000
2023-09-22 11:30:00+00:00  54.250000
2023-09-22 12:00:00+00:00  54.500000
2023-09-22 12:30:00+00:00  54.400002
2023-09-22 13:00:00+00:00  54.550000
2023-09-22 13:30:00+00:00  54.200001
2023-09-22 14:00:00+00:00  54.100000
2023-09-22 14:30:00+00:00  54.099998
2023-09-22 15:00:00+00:00  54.450000
Mean of the first 10 values: price    54.815
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  54.60
2023-09-25 10:00:00+03:00  54.60
2023-09-25 11:00:00+03:00  54.45
2023-09-25 12:00:00+03:00  54.85
2023-09-25 13:00:00+03:00  54.95
2023-09-25 14:00:00+03:00  54.85
2023-09-25 15:00:00+03:00  54.65
2023-09-25 16:00:00+03:00  55.00
2023-09-25 17:00:00+03:00  55.00
2023-09-25 18:00:00+03:00  55.20
                              price
2018-01-02 10:00:00+03:00  0.039200
2018-01-02 11:00:00+03:00 -0.101800
2018-01-02 12:00:00+03:00 -0.007700
2018-01-02 13:00:00+03:00  0.054800
2018-01-02 14:00:00+03:00  0.054700
...                             ...
2023-09-22 13:00:00+00:00  0.149998
2023-09-22 13:30:00+00:00 -0.349999
2023-09-22 14:00:00+00:00 -0.100001
2023-09-22 14:30:00+00:00 -0.000002
2023-09-22 15:00:00+00:00  0.350002

[17890 rows x 1 columns]
ADF Statistic: -21.755813672308253
p-value: 0.0
Critical Values: {'1%': -3.4307163795075204, '5%': -2.8617019257919023, '10%': -2.5668561888610237}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17891
Model:                ARIMA(30, 1, 1)   Log Likelihood                3489.444
Date:                Sun, 24 Dec 2023   AIC                          -6914.888
Time:                        22:26:11   BIC                          -6665.544
Sample:                             0   HQIC                         -6832.868
                              - 17891                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.0701      0.005   -223.985      0.000      -1.079      -1.061
ar.L2         -0.1050      0.005    -22.096      0.000      -0.114      -0.096
ar.L3         -0.0284      0.005     -6.158      0.000      -0.037      -0.019
ar.L4         -0.0506      0.006     -8.899      0.000      -0.062      -0.039
ar.L5         -0.0518      0.007     -7.738      0.000      -0.065      -0.039
ar.L6         -0.0250      0.007     -3.630      0.000      -0.038      -0.011
ar.L7         -0.0038      0.007     -0.538      0.591      -0.018       0.010
ar.L8         -0.0069      0.007     -0.997      0.319      -0.021       0.007
ar.L9          0.0064      0.007      0.963      0.336      -0.007       0.019
ar.L10         0.0012      0.007      0.176      0.860      -0.012       0.015
ar.L11         0.0222      0.006      3.454      0.001       0.010       0.035
ar.L12         0.0035      0.007      0.540      0.589      -0.009       0.016
ar.L13        -0.0062      0.007     -0.937      0.349      -0.019       0.007
ar.L14         0.0018      0.006      0.304      0.761      -0.010       0.013
ar.L15         0.0159      0.006      2.741      0.006       0.005       0.027
ar.L16        -0.0191      0.006     -3.265      0.001      -0.031      -0.008
ar.L17        -0.0533      0.005    -10.428      0.000      -0.063      -0.043
ar.L18         0.1099      0.005     21.499      0.000       0.100       0.120
ar.L19         0.1080      0.004     25.429      0.000       0.100       0.116
ar.L20        -0.0784      0.005    -15.125      0.000      -0.089      -0.068
ar.L21        -0.0675      0.005    -12.637      0.000      -0.078      -0.057
ar.L22        -0.0094      0.006     -1.647      0.100      -0.021       0.002
ar.L23         0.0235      0.006      3.945      0.000       0.012       0.035
ar.L24         0.0293      0.006      4.976      0.000       0.018       0.041
ar.L25         0.0204      0.007      3.124      0.002       0.008       0.033
ar.L26         0.0203      0.007      2.787      0.005       0.006       0.035
ar.L27        -0.0101      0.007     -1.426      0.154      -0.024       0.004
ar.L28        -0.0136      0.007     -1.933      0.053      -0.027       0.000
ar.L29        -0.0091      0.007     -1.245      0.213      -0.024       0.005
ar.L30         0.0341      0.005      6.251      0.000       0.023       0.045
ma.L1          0.9294      0.003    271.900      0.000       0.923       0.936
sigma2         0.0397      0.000    286.185      0.000       0.039       0.040
===================================================================================
Ljung-Box (L1) (Q):                   2.38   Jarque-Bera (JB):            350209.07
Prob(Q):                              0.12   Prob(JB):                         0.00
Heteroskedasticity (H):              15.78   Skew:                             0.71
Prob(H) (two-sided):                  0.00   Kurtosis:                        24.63
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891    54.353134
17892    54.423341
17893    54.415687
17894    54.429490
17895    54.411605
17896    54.448196
17897    54.343791
17898    54.448544
17899    54.349230
17900    54.433034
Name: predicted_mean, dtype: float64
       lower price  upper price
17891    53.962407    54.743862
17892    53.908178    54.938504
17893    53.790862    55.040512
17894    53.723253    55.135728
17895    53.629862    55.193348
17896    53.605983    55.290409
17897    53.439609    55.247972
17898    53.491200    55.405889
17899    53.337587    55.360874
17900    53.372286    55.493782

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.46163604567845706
Weighted Mean Absolute Percentage Error (WMAPE): 0.7468662607384804
In [746]:
forecastplusyahoo('HALKB', 30, 1, 3)

                           price short_name
timestamp                                  
2021-12-27 09:00:00+03:00   4.78      HALKB
2021-12-27 10:00:00+03:00   4.80      HALKB
2021-12-27 11:00:00+03:00   4.78      HALKB
2021-12-27 12:00:00+03:00   4.78      HALKB
2021-12-27 13:00:00+03:00   4.78      HALKB
...                          ...        ...
2023-09-22 14:00:00+03:00  15.36      HALKB
2023-09-22 15:00:00+03:00  15.32      HALKB
2023-09-22 16:00:00+03:00  15.32      HALKB
2023-09-22 17:00:00+03:00  15.20      HALKB
2023-09-22 18:00:00+03:00  15.23      HALKB

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for HALKB.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                           price
2021-12-27 06:00:00+00:00   4.78
2021-12-27 06:30:00+00:00   4.78
2021-12-27 07:00:00+00:00   4.80
2021-12-27 07:30:00+00:00   4.79
2021-12-27 08:00:00+00:00   4.78
2021-12-27 08:30:00+00:00   4.79
2021-12-27 09:00:00+00:00   4.78
2021-12-27 09:30:00+00:00   4.76
2021-12-27 10:00:00+00:00   4.78
2021-12-27 10:30:00+00:00   4.78
2021-12-27 11:00:00+00:00   4.74
2021-12-27 11:30:00+00:00   4.72
2021-12-27 12:00:00+00:00   4.73
2021-12-27 12:30:00+00:00   4.72
2021-12-27 13:00:00+00:00   4.74
2021-12-27 13:30:00+00:00   4.72
2021-12-27 14:00:00+00:00   4.70
2021-12-27 15:00:00+00:00   4.70
2021-12-28 06:00:00+00:00   4.71
2021-12-28 06:30:00+00:00   4.66
2021-12-28 07:00:00+00:00   4.67
2021-12-28 07:30:00+00:00   4.66
2021-12-28 08:00:00+00:00   4.65
2021-12-28 08:30:00+00:00   4.65
2021-12-28 09:00:00+00:00   4.66
2021-12-28 09:30:00+00:00   4.64
2021-12-28 10:00:00+00:00   4.66
2021-12-28 10:30:00+00:00   4.65
2021-12-28 11:00:00+00:00   4.64
2021-12-28 11:30:00+00:00   4.66
2021-12-28 12:00:00+00:00   4.65
2021-12-28 12:30:00+00:00   4.65
2021-12-28 13:00:00+00:00   4.64
2021-12-28 13:30:00+00:00   4.57
2021-12-28 14:00:00+00:00   4.57
2021-12-28 15:00:00+00:00   4.58
2021-12-29 06:00:00+00:00   4.58
2021-12-29 06:30:00+00:00   4.55
2021-12-29 07:00:00+00:00   4.55
2021-12-29 07:30:00+00:00   4.59
2021-12-29 08:00:00+00:00   4.61
2021-12-29 08:30:00+00:00   4.59
2021-12-29 09:00:00+00:00   4.58
2021-12-29 09:30:00+00:00   4.58
2021-12-29 10:00:00+00:00   4.60
2021-12-29 10:30:00+00:00   4.58
2021-12-29 11:00:00+00:00   4.59
2021-12-29 11:30:00+00:00   4.61
2021-12-29 12:00:00+00:00   4.66
2021-12-29 12:30:00+00:00   4.66
                           price
2023-09-21 10:00:00+00:00  15.66
2023-09-21 10:30:00+00:00  15.24
2023-09-21 11:00:00+00:00  15.34
2023-09-21 11:30:00+00:00  15.31
2023-09-21 12:00:00+00:00  15.34
2023-09-21 12:30:00+00:00  15.44
2023-09-21 13:00:00+00:00  15.41
2023-09-21 13:30:00+00:00  15.46
2023-09-21 14:00:00+00:00  15.51
2023-09-21 14:30:00+00:00  15.51
2023-09-21 15:00:00+00:00  15.50
2023-09-22 06:00:00+00:00  15.59
2023-09-22 06:30:00+00:00  15.52
2023-09-22 07:00:00+00:00  15.45
2023-09-22 07:30:00+00:00  15.49
2023-09-22 08:00:00+00:00  15.47
2023-09-22 08:30:00+00:00  15.44
2023-09-22 09:00:00+00:00  15.44
2023-09-22 09:30:00+00:00  15.46
2023-09-22 10:00:00+00:00  15.50
2023-09-22 10:30:00+00:00  15.45
2023-09-22 11:00:00+00:00  15.36
2023-09-22 11:30:00+00:00  15.32
2023-09-22 12:00:00+00:00  15.32
2023-09-22 12:30:00+00:00  15.38
2023-09-22 13:00:00+00:00  15.32
2023-09-22 13:30:00+00:00  15.24
2023-09-22 14:00:00+00:00  15.20
2023-09-22 14:30:00+00:00  15.20
2023-09-22 15:00:00+00:00  15.23
Mean of the first 10 values: price    15.467
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  15.32
2023-09-25 10:00:00+03:00  15.44
2023-09-25 11:00:00+03:00  15.45
2023-09-25 12:00:00+03:00  15.47
2023-09-25 13:00:00+03:00  15.67
2023-09-25 14:00:00+03:00  15.47
2023-09-25 15:00:00+03:00  15.41
2023-09-25 16:00:00+03:00  15.50
2023-09-25 17:00:00+03:00  15.49
2023-09-25 18:00:00+03:00  15.45
                                  price
2018-01-02 10:00:00+03:00  3.219000e-01
2018-01-02 11:00:00+03:00 -1.950000e-02
2018-01-02 12:00:00+03:00 -9.760000e-02
2018-01-02 13:00:00+03:00  1.950000e-02
2018-01-02 14:00:00+03:00  5.860000e-02
...                                 ...
2023-09-22 13:00:00+00:00 -6.000011e-02
2023-09-22 13:30:00+00:00 -8.000023e-02
2023-09-22 14:00:00+00:00 -3.999977e-02
2023-09-22 14:30:00+00:00 -1.907349e-07
2023-09-22 15:00:00+00:00  3.000019e-02

[17889 rows x 1 columns]
ADF Statistic: -19.19262223125624
p-value: 0.0
Critical Values: {'1%': -3.4307164205666516, '5%': -2.861701943937355, '10%': -2.5668561985195675}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17890
Model:                ARIMA(30, 3, 1)   Log Likelihood               20270.570
Date:                Sun, 24 Dec 2023   AIC                         -40477.139
Time:                        22:34:03   BIC                         -40227.800
Sample:                             0   HQIC                        -40395.120
                              - 17890                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.0194      0.004   -256.005      0.000      -1.027      -1.012
ar.L2         -0.9272      0.007   -136.216      0.000      -0.940      -0.914
ar.L3         -0.9346      0.009   -108.890      0.000      -0.951      -0.918
ar.L4         -0.9026      0.011    -83.117      0.000      -0.924      -0.881
ar.L5         -0.9102      0.013    -72.630      0.000      -0.935      -0.886
ar.L6         -0.8482      0.014    -60.362      0.000      -0.876      -0.821
ar.L7         -0.8666      0.015    -56.563      0.000      -0.897      -0.837
ar.L8         -0.8212      0.016    -50.451      0.000      -0.853      -0.789
ar.L9         -0.8293      0.017    -48.850      0.000      -0.863      -0.796
ar.L10        -0.7765      0.018    -43.796      0.000      -0.811      -0.742
ar.L11        -0.7690      0.019    -41.494      0.000      -0.805      -0.733
ar.L12        -0.7222      0.019    -38.381      0.000      -0.759      -0.685
ar.L13        -0.7280      0.019    -37.461      0.000      -0.766      -0.690
ar.L14        -0.6596      0.020    -33.337      0.000      -0.698      -0.621
ar.L15        -0.6472      0.020    -32.758      0.000      -0.686      -0.608
ar.L16        -0.5979      0.019    -31.036      0.000      -0.636      -0.560
ar.L17        -0.5934      0.019    -31.331      0.000      -0.631      -0.556
ar.L18        -0.5185      0.019    -27.382      0.000      -0.556      -0.481
ar.L19        -0.4771      0.018    -26.287      0.000      -0.513      -0.442
ar.L20        -0.3913      0.018    -22.296      0.000      -0.426      -0.357
ar.L21        -0.3865      0.017    -23.269      0.000      -0.419      -0.354
ar.L22        -0.3226      0.016    -20.801      0.000      -0.353      -0.292
ar.L23        -0.3157      0.015    -21.324      0.000      -0.345      -0.287
ar.L24        -0.2377      0.014    -17.254      0.000      -0.265      -0.211
ar.L25        -0.2236      0.013    -17.254      0.000      -0.249      -0.198
ar.L26        -0.1693      0.011    -14.751      0.000      -0.192      -0.147
ar.L27        -0.1594      0.011    -14.765      0.000      -0.181      -0.138
ar.L28        -0.0733      0.010     -7.554      0.000      -0.092      -0.054
ar.L29        -0.0819      0.008    -10.861      0.000      -0.097      -0.067
ar.L30         0.0201      0.006      3.519      0.000       0.009       0.031
ma.L1         -0.9516      0.003   -365.474      0.000      -0.957      -0.946
sigma2         0.0060   1.32e-05    457.634      0.000       0.006       0.006
===================================================================================
Ljung-Box (L1) (Q):                   0.12   Jarque-Bera (JB):           2825209.23
Prob(Q):                              0.73   Prob(JB):                         0.00
Heteroskedasticity (H):               3.67   Skew:                             0.42
Prob(H) (two-sided):                  0.00   Kurtosis:                        64.56
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890    15.250316
17891    15.198568
17892    15.209644
17893    15.201208
17894    15.194959
17895    15.195164
17896    15.178850
17897    15.167375
17898    15.161806
17899    15.145719
Name: predicted_mean, dtype: float64
       lower price  upper price
17890    15.098100    15.402532
17891    14.980153    15.416984
17892    14.927905    15.491383
17893    14.864602    15.537815
17894    14.804309    15.585609
17895    14.754110    15.636219
17896    14.684471    15.673228
17897    14.622806    15.711944
17898    14.564311    15.759300
17899    14.497152    15.794285

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.29304138553825776
Weighted Mean Absolute Percentage Error (WMAPE): 1.7885766417985445
In [747]:
forecastplusyahoo('ISCTR', 30, 1, 1)

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00   3.1299      ISCTR
2021-12-27 10:00:00+03:00   3.1702      ISCTR
2021-12-27 11:00:00+03:00   3.1823      ISCTR
2021-12-27 12:00:00+03:00   3.1984      ISCTR
2021-12-27 13:00:00+03:00   3.1864      ISCTR
...                            ...        ...
2023-09-22 14:00:00+03:00  24.2400      ISCTR
2023-09-22 15:00:00+03:00  24.0800      ISCTR
2023-09-22 16:00:00+03:00  24.7800      ISCTR
2023-09-22 17:00:00+03:00  24.9600      ISCTR
2023-09-22 18:00:00+03:00  24.9000      ISCTR

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for ISCTR.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                              price
2021-12-27 06:00:00+00:00  3.129900
2021-12-27 06:30:00+00:00  3.510071
2021-12-27 07:00:00+00:00  3.170200
2021-12-27 07:30:00+00:00  3.541572
2021-12-27 08:00:00+00:00  3.182300
2021-12-27 08:30:00+00:00  3.564072
2021-12-27 09:00:00+00:00  3.198400
2021-12-27 09:30:00+00:00  3.546072
2021-12-27 10:00:00+00:00  3.186400
2021-12-27 10:30:00+00:00  3.559572
2021-12-27 11:00:00+00:00  3.162100
2021-12-27 11:30:00+00:00  3.537072
2021-12-27 12:00:00+00:00  3.182300
2021-12-27 12:30:00+00:00  3.541572
2021-12-27 13:00:00+00:00  3.190400
2021-12-27 13:30:00+00:00  3.577573
2021-12-27 14:00:00+00:00  3.214600
2021-12-27 15:00:00+00:00  3.214600
2021-12-28 06:00:00+00:00  3.194300
2021-12-28 06:30:00+00:00  3.469570
2021-12-28 07:00:00+00:00  3.105600
2021-12-28 07:30:00+00:00  3.460570
2021-12-28 08:00:00+00:00  3.081500
2021-12-28 08:30:00+00:00  3.429070
2021-12-28 09:00:00+00:00  3.073400
2021-12-28 09:30:00+00:00  3.429070
2021-12-28 10:00:00+00:00  3.077400
2021-12-28 10:30:00+00:00  3.438070
2021-12-28 11:00:00+00:00  3.065300
2021-12-28 11:30:00+00:00  3.429070
2021-12-28 12:00:00+00:00  3.073400
2021-12-28 12:30:00+00:00  3.420069
2021-12-28 13:00:00+00:00  3.049200
2021-12-28 13:30:00+00:00  3.366068
2021-12-28 14:00:00+00:00  3.008900
2021-12-28 15:00:00+00:00  3.008900
2021-12-29 06:00:00+00:00  2.976600
2021-12-29 06:30:00+00:00  3.294067
2021-12-29 07:00:00+00:00  2.948400
2021-12-29 07:30:00+00:00  3.334568
2021-12-29 08:00:00+00:00  3.000800
2021-12-29 08:30:00+00:00  3.352568
2021-12-29 09:00:00+00:00  2.996800
2021-12-29 09:30:00+00:00  3.334568
2021-12-29 10:00:00+00:00  3.000800
2021-12-29 10:30:00+00:00  3.343568
2021-12-29 11:00:00+00:00  3.004900
2021-12-29 11:30:00+00:00  3.384069
2021-12-29 12:00:00+00:00  3.025000
2021-12-29 12:30:00+00:00  3.411069
                               price
2023-09-21 10:00:00+00:00  22.960000
2023-09-21 10:30:00+00:00  22.440001
2023-09-21 11:00:00+00:00  22.660000
2023-09-21 11:30:00+00:00  22.620001
2023-09-21 12:00:00+00:00  22.720000
2023-09-21 12:30:00+00:00  23.020000
2023-09-21 13:00:00+00:00  22.980000
2023-09-21 13:30:00+00:00  22.940001
2023-09-21 14:00:00+00:00  23.000000
2023-09-21 14:30:00+00:00  23.000000
2023-09-21 15:00:00+00:00  23.140000
2023-09-22 06:00:00+00:00  23.180000
2023-09-22 06:30:00+00:00  23.080000
2023-09-22 07:00:00+00:00  23.160000
2023-09-22 07:30:00+00:00  23.540001
2023-09-22 08:00:00+00:00  23.580000
2023-09-22 08:30:00+00:00  23.540001
2023-09-22 09:00:00+00:00  23.480000
2023-09-22 09:30:00+00:00  23.799999
2023-09-22 10:00:00+00:00  24.400000
2023-09-22 10:30:00+00:00  24.480000
2023-09-22 11:00:00+00:00  24.240000
2023-09-22 11:30:00+00:00  24.100000
2023-09-22 12:00:00+00:00  24.080000
2023-09-22 12:30:00+00:00  24.920000
2023-09-22 13:00:00+00:00  24.780000
2023-09-22 13:30:00+00:00  25.040001
2023-09-22 14:00:00+00:00  24.960000
2023-09-22 14:30:00+00:00  24.959999
2023-09-22 15:00:00+00:00  24.900000
Mean of the first 10 values: price    25.218
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  25.04
2023-09-25 10:00:00+03:00  25.04
2023-09-25 11:00:00+03:00  25.28
2023-09-25 12:00:00+03:00  25.20
2023-09-25 13:00:00+03:00  25.24
2023-09-25 14:00:00+03:00  25.40
2023-09-25 15:00:00+03:00  25.32
2023-09-25 16:00:00+03:00  25.28
2023-09-25 17:00:00+03:00  25.22
2023-09-25 18:00:00+03:00  25.16
                                  price
2018-01-02 10:00:00+03:00  1.130000e-02
2018-01-02 11:00:00+03:00  0.000000e+00
2018-01-02 12:00:00+03:00 -7.500000e-03
2018-01-02 13:00:00+03:00  0.000000e+00
2018-01-02 14:00:00+03:00  3.000000e-02
...                                 ...
2023-09-22 13:00:00+00:00 -1.400001e-01
2023-09-22 13:30:00+00:00  2.600009e-01
2023-09-22 14:00:00+00:00 -8.000092e-02
2023-09-22 14:30:00+00:00 -9.155273e-07
2023-09-22 15:00:00+00:00 -5.999908e-02

[17890 rows x 1 columns]
ADF Statistic: -17.421209351684237
p-value: 4.824718674506475e-30
Critical Values: {'1%': -3.4307164410996687, '5%': -2.8617019530116066, '10%': -2.5668562033496514}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17891
Model:                ARIMA(30, 1, 1)   Log Likelihood               12847.278
Date:                Sun, 24 Dec 2023   AIC                         -25630.557
Time:                        22:36:52   BIC                         -25381.213
Sample:                             0   HQIC                        -25548.537
                              - 17891                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.4334      0.104     -4.173      0.000      -0.637      -0.230
ar.L2          0.2290      0.075      3.041      0.002       0.081       0.377
ar.L3         -0.0700      0.005    -14.434      0.000      -0.080      -0.061
ar.L4         -0.0051      0.008     -0.601      0.548      -0.022       0.011
ar.L5         -0.0313      0.005     -5.843      0.000      -0.042      -0.021
ar.L6          0.0515      0.006      7.977      0.000       0.039       0.064
ar.L7         -0.0235      0.007     -3.178      0.001      -0.038      -0.009
ar.L8          0.0228      0.006      3.525      0.000       0.010       0.035
ar.L9         -0.0222      0.007     -3.387      0.001      -0.035      -0.009
ar.L10         0.0406      0.006      6.579      0.000       0.028       0.053
ar.L11         0.0044      0.007      0.648      0.517      -0.009       0.018
ar.L12        -0.0174      0.006     -2.728      0.006      -0.030      -0.005
ar.L13         0.0020      0.005      0.361      0.718      -0.009       0.013
ar.L14        -0.0103      0.005     -2.151      0.031      -0.020      -0.001
ar.L15         0.0144      0.005      2.958      0.003       0.005       0.024
ar.L16        -0.1499      0.004    -38.343      0.000      -0.158      -0.142
ar.L17        -0.0405      0.015     -2.633      0.008      -0.071      -0.010
ar.L18         0.3513      0.009     39.244      0.000       0.334       0.369
ar.L19         0.2959      0.034      8.711      0.000       0.229       0.363
ar.L20        -0.2254      0.041     -5.539      0.000      -0.305      -0.146
ar.L21        -0.0543      0.012     -4.448      0.000      -0.078      -0.030
ar.L22         0.0541      0.010      5.437      0.000       0.035       0.074
ar.L23        -0.0477      0.005     -9.074      0.000      -0.058      -0.037
ar.L24         0.0085      0.006      1.308      0.191      -0.004       0.021
ar.L25        -0.0264      0.005     -4.990      0.000      -0.037      -0.016
ar.L26         0.0404      0.007      6.146      0.000       0.027       0.053
ar.L27        -0.0210      0.007     -2.878      0.004      -0.035      -0.007
ar.L28         0.0227      0.006      3.601      0.000       0.010       0.035
ar.L29        -0.0418      0.006     -6.715      0.000      -0.054      -0.030
ar.L30         0.0631      0.007      8.899      0.000       0.049       0.077
ma.L1         -0.2904      0.104     -2.793      0.005      -0.494      -0.087
sigma2         0.0139   5.32e-05    261.900      0.000       0.014       0.014
===================================================================================
Ljung-Box (L1) (Q):                   0.09   Jarque-Bera (JB):            354289.18
Prob(Q):                              0.77   Prob(JB):                         0.00
Heteroskedasticity (H):              77.80   Skew:                            -0.09
Prob(H) (two-sided):                  0.00   Kurtosis:                        24.80
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17891    24.929343
17892    24.777142
17893    25.082632
17894    25.000581
17895    25.025407
17896    24.824054
17897    25.010176
17898    25.197724
17899    25.349444
17900    25.096077
Name: predicted_mean, dtype: float64
       lower price  upper price
17891    24.698095    25.160590
17892    24.537235    25.017048
17893    24.776988    25.388276
17894    24.684525    25.316637
17895    24.667965    25.382849
17896    24.455618    25.192490
17897    24.605687    25.414665
17898    24.782900    25.612548
17899    24.902514    25.796375
17900    24.639836    25.552319

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.25718235592753785
Weighted Mean Absolute Percentage Error (WMAPE): 0.9003640330858422
In [752]:
forecastplusyahoo('VAKBN', 30, 1, 3)

                           price short_name
timestamp                                  
2021-12-27 09:00:00+03:00   3.86      VAKBN
2021-12-27 10:00:00+03:00   3.87      VAKBN
2021-12-27 11:00:00+03:00   3.86      VAKBN
2021-12-27 12:00:00+03:00   3.86      VAKBN
2021-12-27 13:00:00+03:00   3.85      VAKBN
...                          ...        ...
2023-09-22 14:00:00+03:00  13.58      VAKBN
2023-09-22 15:00:00+03:00  13.53      VAKBN
2023-09-22 16:00:00+03:00  13.56      VAKBN
2023-09-22 17:00:00+03:00  13.49      VAKBN
2023-09-22 18:00:00+03:00  13.50      VAKBN

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for VAKBN.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                           price
2021-12-27 06:00:00+00:00   3.86
2021-12-27 06:30:00+00:00   3.85
2021-12-27 07:00:00+00:00   3.87
2021-12-27 07:30:00+00:00   3.86
2021-12-27 08:00:00+00:00   3.86
2021-12-27 08:30:00+00:00   3.86
2021-12-27 09:00:00+00:00   3.86
2021-12-27 09:30:00+00:00   3.84
2021-12-27 10:00:00+00:00   3.85
2021-12-27 10:30:00+00:00   3.85
2021-12-27 11:00:00+00:00   3.83
2021-12-27 11:30:00+00:00   3.83
2021-12-27 12:00:00+00:00   3.84
2021-12-27 12:30:00+00:00   3.84
2021-12-27 13:00:00+00:00   3.86
2021-12-27 13:30:00+00:00   3.85
2021-12-27 14:00:00+00:00   3.83
2021-12-27 15:00:00+00:00   3.83
2021-12-28 06:00:00+00:00   3.84
2021-12-28 06:30:00+00:00   3.79
2021-12-28 07:00:00+00:00   3.78
2021-12-28 07:30:00+00:00   3.79
2021-12-28 08:00:00+00:00   3.78
2021-12-28 08:30:00+00:00   3.77
2021-12-28 09:00:00+00:00   3.77
2021-12-28 09:30:00+00:00   3.76
2021-12-28 10:00:00+00:00   3.77
2021-12-28 10:30:00+00:00   3.77
2021-12-28 11:00:00+00:00   3.77
2021-12-28 11:30:00+00:00   3.77
2021-12-28 12:00:00+00:00   3.78
2021-12-28 12:30:00+00:00   3.76
2021-12-28 13:00:00+00:00   3.76
2021-12-28 13:30:00+00:00   3.68
2021-12-28 14:00:00+00:00   3.68
2021-12-28 15:00:00+00:00   3.69
2021-12-29 06:00:00+00:00   3.65
2021-12-29 06:30:00+00:00   3.65
2021-12-29 07:00:00+00:00   3.66
2021-12-29 07:30:00+00:00   3.73
2021-12-29 08:00:00+00:00   3.74
2021-12-29 08:30:00+00:00   3.72
2021-12-29 09:00:00+00:00   3.73
2021-12-29 09:30:00+00:00   3.73
2021-12-29 10:00:00+00:00   3.73
2021-12-29 10:30:00+00:00   3.72
2021-12-29 11:00:00+00:00   3.74
2021-12-29 11:30:00+00:00   3.74
2021-12-29 12:00:00+00:00   3.75
2021-12-29 12:30:00+00:00   3.75
                           price
2023-09-21 10:00:00+00:00  13.69
2023-09-21 10:30:00+00:00  13.47
2023-09-21 11:00:00+00:00  13.52
2023-09-21 11:30:00+00:00  13.49
2023-09-21 12:00:00+00:00  13.49
2023-09-21 12:30:00+00:00  13.66
2023-09-21 13:00:00+00:00  13.59
2023-09-21 13:30:00+00:00  13.61
2023-09-21 14:00:00+00:00  13.63
2023-09-21 14:30:00+00:00  13.63
2023-09-21 15:00:00+00:00  13.64
2023-09-22 06:00:00+00:00  13.65
2023-09-22 06:30:00+00:00  13.69
2023-09-22 07:00:00+00:00  13.69
2023-09-22 07:30:00+00:00  13.76
2023-09-22 08:00:00+00:00  13.75
2023-09-22 08:30:00+00:00  13.71
2023-09-22 09:00:00+00:00  13.70
2023-09-22 09:30:00+00:00  13.73
2023-09-22 10:00:00+00:00  13.79
2023-09-22 10:30:00+00:00  13.70
2023-09-22 11:00:00+00:00  13.58
2023-09-22 11:30:00+00:00  13.52
2023-09-22 12:00:00+00:00  13.53
2023-09-22 12:30:00+00:00  13.63
2023-09-22 13:00:00+00:00  13.56
2023-09-22 13:30:00+00:00  13.49
2023-09-22 14:00:00+00:00  13.49
2023-09-22 14:30:00+00:00  13.49
2023-09-22 15:00:00+00:00  13.50
Mean of the first 10 values: price    13.799
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  13.59
2023-09-25 10:00:00+03:00  13.64
2023-09-25 11:00:00+03:00  13.72
2023-09-25 12:00:00+03:00  13.85
2023-09-25 13:00:00+03:00  13.93
2023-09-25 14:00:00+03:00  13.85
2023-09-25 15:00:00+03:00  13.80
2023-09-25 16:00:00+03:00  13.86
2023-09-25 17:00:00+03:00  13.85
2023-09-25 18:00:00+03:00  13.90
                                  price
2018-01-02 10:00:00+03:00  1.085000e-01
2018-01-02 11:00:00+03:00  7.880000e-02
2018-01-02 12:00:00+03:00  0.000000e+00
2018-01-02 13:00:00+03:00  9.900000e-03
2018-01-02 14:00:00+03:00  3.940000e-02
...                                 ...
2023-09-22 13:00:00+00:00 -7.000011e-02
2023-09-22 13:30:00+00:00 -7.000023e-02
2023-09-22 14:00:00+00:00  2.288818e-07
2023-09-22 14:30:00+00:00 -2.288818e-07
2023-09-22 15:00:00+00:00  1.000023e-02

[17889 rows x 1 columns]
ADF Statistic: -18.14892823532758
p-value: 2.4790136776995764e-30
Critical Values: {'1%': -3.4307164410996687, '5%': -2.8617019530116066, '10%': -2.5668562033496514}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17890
Model:                ARIMA(30, 3, 1)   Log Likelihood               23168.626
Date:                Sun, 24 Dec 2023   AIC                         -46273.252
Time:                        22:47:21   BIC                         -46023.913
Sample:                             0   HQIC                        -46191.233
                              - 17890                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.0633      0.004   -287.864      0.000      -1.071      -1.056
ar.L2         -0.9828      0.006   -172.024      0.000      -0.994      -0.972
ar.L3         -0.9560      0.007   -128.643      0.000      -0.971      -0.941
ar.L4         -0.8979      0.009   -100.018      0.000      -0.915      -0.880
ar.L5         -0.8795      0.010    -86.046      0.000      -0.900      -0.859
ar.L6         -0.8215      0.011    -72.137      0.000      -0.844      -0.799
ar.L7         -0.8304      0.012    -67.613      0.000      -0.854      -0.806
ar.L8         -0.7807      0.013    -58.841      0.000      -0.807      -0.755
ar.L9         -0.7890      0.013    -58.569      0.000      -0.815      -0.763
ar.L10        -0.7314      0.014    -52.916      0.000      -0.759      -0.704
ar.L11        -0.7273      0.014    -50.358      0.000      -0.756      -0.699
ar.L12        -0.6694      0.015    -45.229      0.000      -0.698      -0.640
ar.L13        -0.6932      0.014    -48.770      0.000      -0.721      -0.665
ar.L14        -0.6221      0.014    -44.261      0.000      -0.650      -0.595
ar.L15        -0.6113      0.014    -44.457      0.000      -0.638      -0.584
ar.L16        -0.5564      0.014    -40.783      0.000      -0.583      -0.530
ar.L17        -0.5728      0.013    -43.196      0.000      -0.599      -0.547
ar.L18        -0.4914      0.013    -37.028      0.000      -0.517      -0.465
ar.L19        -0.4182      0.013    -32.198      0.000      -0.444      -0.393
ar.L20        -0.3467      0.013    -25.815      0.000      -0.373      -0.320
ar.L21        -0.3779      0.014    -27.805      0.000      -0.405      -0.351
ar.L22        -0.3141      0.014    -23.090      0.000      -0.341      -0.287
ar.L23        -0.3222      0.014    -23.135      0.000      -0.350      -0.295
ar.L24        -0.2502      0.013    -19.373      0.000      -0.275      -0.225
ar.L25        -0.2252      0.012    -18.158      0.000      -0.249      -0.201
ar.L26        -0.1578      0.011    -14.050      0.000      -0.180      -0.136
ar.L27        -0.1595      0.010    -15.589      0.000      -0.180      -0.139
ar.L28        -0.0875      0.009     -9.861      0.000      -0.105      -0.070
ar.L29        -0.0836      0.007    -11.955      0.000      -0.097      -0.070
ar.L30        -0.0017      0.005     -0.345      0.730      -0.011       0.008
ma.L1         -0.9772      0.002   -562.780      0.000      -0.981      -0.974
sigma2         0.0044   9.97e-06    438.595      0.000       0.004       0.004
===================================================================================
Ljung-Box (L1) (Q):                   0.02   Jarque-Bera (JB):           4231751.69
Prob(Q):                              0.89   Prob(JB):                         0.00
Heteroskedasticity (H):               6.42   Skew:                             0.45
Prob(H) (two-sided):                  0.00   Kurtosis:                        78.35
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890    13.504667
17891    13.485549
17892    13.486290
17893    13.496010
17894    13.493438
17895    13.493881
17896    13.485630
17897    13.482546
17898    13.491759
17899    13.477176
Name: predicted_mean, dtype: float64
       lower price  upper price
17890    13.375033    13.634301
17891    13.305888    13.665210
17892    13.259670    13.712909
17893    13.227902    13.764119
17894    13.184049    13.802827
17895    13.145791    13.841972
17896    13.097622    13.873638
17897    13.057545    13.907546
17898    13.028089    13.955429
17899    12.977063    13.977289

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.3281600088901808
Weighted Mean Absolute Percentage Error (WMAPE): 2.241505848139771
In [749]:
forecastplusyahoo('VESTL', 30, 1, 1)

                           price short_name
timestamp                                  
2021-12-27 09:00:00+03:00  24.94      VESTL
2021-12-27 10:00:00+03:00  25.44      VESTL
2021-12-27 11:00:00+03:00  25.16      VESTL
2021-12-27 12:00:00+03:00  24.98      VESTL
2021-12-27 13:00:00+03:00  25.08      VESTL
...                          ...        ...
2023-09-22 14:00:00+03:00  63.10      VESTL
2023-09-22 15:00:00+03:00  62.55      VESTL
2023-09-22 16:00:00+03:00  64.15      VESTL
2023-09-22 17:00:00+03:00  64.20      VESTL
2023-09-22 18:00:00+03:00  64.10      VESTL

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for VESTL.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                               price
2021-12-27 06:00:00+00:00  24.940000
2021-12-27 06:30:00+00:00  25.379999
2021-12-27 07:00:00+00:00  25.440000
2021-12-27 07:30:00+00:00  25.139999
2021-12-27 08:00:00+00:00  25.160000
2021-12-27 08:30:00+00:00  25.080000
2021-12-27 09:00:00+00:00  24.980000
2021-12-27 09:30:00+00:00  24.900000
2021-12-27 10:00:00+00:00  25.080000
2021-12-27 10:30:00+00:00  25.020000
2021-12-27 11:00:00+00:00  24.680000
2021-12-27 11:30:00+00:00  24.540001
2021-12-27 12:00:00+00:00  24.620000
2021-12-27 12:30:00+00:00  24.620001
2021-12-27 13:00:00+00:00  24.700000
2021-12-27 13:30:00+00:00  24.860001
2021-12-27 14:00:00+00:00  24.720000
2021-12-27 15:00:00+00:00  24.680000
2021-12-28 06:00:00+00:00  24.840000
2021-12-28 06:30:00+00:00  24.660000
2021-12-28 07:00:00+00:00  24.740000
2021-12-28 07:30:00+00:00  24.680000
2021-12-28 08:00:00+00:00  24.500000
2021-12-28 08:30:00+00:00  24.459999
2021-12-28 09:00:00+00:00  24.420000
2021-12-28 09:30:00+00:00  24.420000
2021-12-28 10:00:00+00:00  24.320000
2021-12-28 10:30:00+00:00  24.320000
2021-12-28 11:00:00+00:00  24.260000
2021-12-28 11:30:00+00:00  24.320000
2021-12-28 12:00:00+00:00  24.260000
2021-12-28 12:30:00+00:00  24.260000
2021-12-28 13:00:00+00:00  24.100000
2021-12-28 13:30:00+00:00  23.900000
2021-12-28 14:00:00+00:00  23.840000
2021-12-28 15:00:00+00:00  23.800000
2021-12-29 06:00:00+00:00  23.880000
2021-12-29 06:30:00+00:00  23.660000
2021-12-29 07:00:00+00:00  23.660000
2021-12-29 07:30:00+00:00  24.059999
2021-12-29 08:00:00+00:00  24.080000
2021-12-29 08:30:00+00:00  24.100000
2021-12-29 09:00:00+00:00  24.160000
2021-12-29 09:30:00+00:00  24.139999
2021-12-29 10:00:00+00:00  24.160000
2021-12-29 10:30:00+00:00  24.120001
2021-12-29 11:00:00+00:00  24.160000
2021-12-29 11:30:00+00:00  24.299999
2021-12-29 12:00:00+00:00  24.320000
2021-12-29 12:30:00+00:00  24.379999
                               price
2023-09-21 10:00:00+00:00  59.350000
2023-09-21 10:30:00+00:00  60.099998
2023-09-21 11:00:00+00:00  60.350000
2023-09-21 11:30:00+00:00  60.250000
2023-09-21 12:00:00+00:00  60.050000
2023-09-21 12:30:00+00:00  60.549999
2023-09-21 13:00:00+00:00  60.950000
2023-09-21 13:30:00+00:00  61.250000
2023-09-21 14:00:00+00:00  61.700000
2023-09-21 14:30:00+00:00  61.700001
2023-09-21 15:00:00+00:00  61.900000
2023-09-22 06:00:00+00:00  62.050000
2023-09-22 06:30:00+00:00  62.950001
2023-09-22 07:00:00+00:00  62.550000
2023-09-22 07:30:00+00:00  62.799999
2023-09-22 08:00:00+00:00  63.400000
2023-09-22 08:30:00+00:00  63.150002
2023-09-22 09:00:00+00:00  63.100000
2023-09-22 09:30:00+00:00  62.650002
2023-09-22 10:00:00+00:00  63.100000
2023-09-22 10:30:00+00:00  63.250000
2023-09-22 11:00:00+00:00  63.100000
2023-09-22 11:30:00+00:00  62.599998
2023-09-22 12:00:00+00:00  62.550000
2023-09-22 12:30:00+00:00  63.299999
2023-09-22 13:00:00+00:00  64.150000
2023-09-22 13:30:00+00:00  64.050003
2023-09-22 14:00:00+00:00  64.200000
2023-09-22 14:30:00+00:00  64.199997
2023-09-22 15:00:00+00:00  64.100000
Mean of the first 10 values: price    64.705
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  64.40
2023-09-25 10:00:00+03:00  65.10
2023-09-25 11:00:00+03:00  64.80
2023-09-25 12:00:00+03:00  64.75
2023-09-25 13:00:00+03:00  65.00
2023-09-25 14:00:00+03:00  64.45
2023-09-25 15:00:00+03:00  64.30
2023-09-25 16:00:00+03:00  64.70
2023-09-25 17:00:00+03:00  64.80
2023-09-25 18:00:00+03:00  64.75
                              price
2018-01-02 10:00:00+03:00  0.173300
2018-01-02 11:00:00+03:00  0.315000
2018-01-02 12:00:00+03:00  0.039300
2018-01-02 13:00:00+03:00  0.000000
2018-01-02 14:00:00+03:00  0.322900
...                             ...
2023-09-22 13:00:00+00:00  0.850001
2023-09-22 13:30:00+00:00 -0.099997
2023-09-22 14:00:00+00:00  0.149997
2023-09-22 14:30:00+00:00 -0.000003
2023-09-22 15:00:00+00:00 -0.099997

[17889 rows x 1 columns]
ADF Statistic: -26.126464966641958
p-value: 0.0
Critical Values: {'1%': -3.430716174349812, '5%': -2.8617018351255985, '10%': -2.566856140600752}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17890
Model:                ARIMA(30, 1, 1)   Log Likelihood               -2459.988
Date:                Sun, 24 Dec 2023   AIC                           4983.975
Time:                        22:38:08   BIC                           5233.317
Sample:                             0   HQIC                          5065.995
                              - 17890                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          0.0023      8.536      0.000      1.000     -16.728      16.732
ar.L2         -0.0468      0.043     -1.092      0.275      -0.131       0.037
ar.L3          0.0068      0.399      0.017      0.986      -0.776       0.790
ar.L4         -0.0009      0.059     -0.015      0.988      -0.117       0.116
ar.L5          0.0101      0.009      1.131      0.258      -0.007       0.028
ar.L6          0.0111      0.086      0.128      0.898      -0.158       0.181
ar.L7          0.0013      0.095      0.014      0.989      -0.184       0.187
ar.L8          0.0096      0.011      0.864      0.388      -0.012       0.032
ar.L9          0.0286      0.082      0.347      0.729      -0.133       0.190
ar.L10        -0.0062      0.244     -0.025      0.980      -0.484       0.471
ar.L11         0.0128      0.053      0.240      0.810      -0.092       0.117
ar.L12        -0.0293      0.110     -0.267      0.789      -0.244       0.185
ar.L13         0.0343      0.250      0.137      0.891      -0.455       0.524
ar.L14         0.0218      0.294      0.074      0.941      -0.554       0.598
ar.L15         0.0145      0.185      0.078      0.938      -0.349       0.378
ar.L16        -0.0141      0.123     -0.115      0.909      -0.255       0.227
ar.L17         0.0053      0.121      0.044      0.965      -0.231       0.242
ar.L18         0.0158      0.045      0.349      0.727      -0.073       0.105
ar.L19         0.0054      0.135      0.040      0.968      -0.259       0.270
ar.L20        -0.0304      0.046     -0.661      0.509      -0.121       0.060
ar.L21         0.0080      0.260      0.031      0.975      -0.501       0.517
ar.L22        -0.0013      0.070     -0.018      0.985      -0.138       0.135
ar.L23        -0.0046      0.011     -0.400      0.689      -0.027       0.018
ar.L24        -0.0203      0.039     -0.518      0.604      -0.097       0.057
ar.L25        -0.0122      0.173     -0.070      0.944      -0.352       0.327
ar.L26        -0.0098      0.104     -0.094      0.925      -0.214       0.194
ar.L27        -0.0100      0.083     -0.120      0.904      -0.173       0.153
ar.L28        -0.0139      0.085     -0.163      0.871      -0.181       0.153
ar.L29         0.0086      0.119      0.072      0.942      -0.224       0.241
ar.L30        -0.0005      0.074     -0.007      0.995      -0.145       0.144
ma.L1          0.0028      8.536      0.000      1.000     -16.728      16.733
sigma2         0.0771      0.000    380.802      0.000       0.077       0.077
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           2712737.08
Prob(Q):                              0.99   Prob(JB):                         0.00
Heteroskedasticity (H):              24.03   Skew:                             1.49
Prob(H) (two-sided):                  0.00   Kurtosis:                        63.25
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17890    64.120903
17891    64.065305
17892    64.034669
17893    64.082778
17894    64.100479
17895    64.041660
17896    64.000511
17897    63.994282
17898    64.051274
17899    64.034090
Name: predicted_mean, dtype: float64
       lower price  upper price
17890    63.576747    64.665059
17891    63.293820    64.836790
17892    63.103492    64.965845
17893    63.013813    65.151742
17894    62.909247    65.291710
17895    62.737494    65.345825
17896    62.590140    65.410881
17897    62.485075    65.503490
17898    62.447714    65.654834
17899    62.336365    65.731815

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.6939838468610346
Weighted Mean Absolute Percentage Error (WMAPE): 1.0082760508349327
In [750]:
forecastplusyahoo('YKBNK', 30, 1, 1)

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00   3.1711      YKBNK
2021-12-27 10:00:00+03:00   3.2338      YKBNK
2021-12-27 11:00:00+03:00   3.2517      YKBNK
2021-12-27 12:00:00+03:00   3.2517      YKBNK
2021-12-27 13:00:00+03:00   3.2517      YKBNK
...                            ...        ...
2023-09-22 14:00:00+03:00  16.9100      YKBNK
2023-09-22 15:00:00+03:00  16.9300      YKBNK
2023-09-22 16:00:00+03:00  16.9900      YKBNK
2023-09-22 17:00:00+03:00  16.8100      YKBNK
2023-09-22 18:00:00+03:00  16.8000      YKBNK

[4324 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
Stock data for YKBNK.IS:
                                 Open        High         Low       Close  \
Datetime                                                                    
2023-09-25 09:30:00+03:00  138.000000  140.199997  138.000000  139.300003   
2023-09-25 10:30:00+03:00  139.399994  140.100006  139.100006  140.100006   
2023-09-25 11:30:00+03:00  140.000000  141.100006  139.399994  140.899994   
2023-09-25 12:30:00+03:00  140.899994  141.899994  140.600006  141.899994   
2023-09-25 13:30:00+03:00  141.899994  142.699997  141.300003  142.500000   
2023-09-25 14:30:00+03:00  142.500000  143.199997  141.800003  142.100006   
2023-09-25 15:30:00+03:00  142.100006  143.100006  141.899994  143.000000   
2023-09-25 16:30:00+03:00  143.000000  143.500000  142.199997  142.800003   
2023-09-25 17:30:00+03:00  142.899994  143.000000  142.100006  142.300003   

                            Adj Close   Volume  
Datetime                                        
2023-09-25 09:30:00+03:00  139.300003        0  
2023-09-25 10:30:00+03:00  140.100006  1997811  
2023-09-25 11:30:00+03:00  140.899994  2334550  
2023-09-25 12:30:00+03:00  141.899994  2153753  
2023-09-25 13:30:00+03:00  142.500000  1863498  
2023-09-25 14:30:00+03:00  142.100006  2693308  
2023-09-25 15:30:00+03:00  143.000000  1611697  
2023-09-25 16:30:00+03:00  142.800003  1813080  
2023-09-25 17:30:00+03:00  142.300003  1375453  

                            price
2021-12-27 06:00:00+00:00  3.1711
2021-12-27 06:30:00+00:00  3.5600
2021-12-27 07:00:00+00:00  3.2338
2021-12-27 07:30:00+00:00  3.6200
2021-12-27 08:00:00+00:00  3.2517
2021-12-27 08:30:00+00:00  3.6500
2021-12-27 09:00:00+00:00  3.2517
2021-12-27 09:30:00+00:00  3.6200
2021-12-27 10:00:00+00:00  3.2517
2021-12-27 10:30:00+00:00  3.6300
2021-12-27 11:00:00+00:00  3.2249
2021-12-27 11:30:00+00:00  3.5900
2021-12-27 12:00:00+00:00  3.2697
2021-12-27 12:30:00+00:00  3.6600
2021-12-27 13:00:00+00:00  3.2965
2021-12-27 13:30:00+00:00  3.6700
2021-12-27 14:00:00+00:00  3.2786
2021-12-27 15:00:00+00:00  3.2786
2021-12-28 06:00:00+00:00  3.2607
2021-12-28 06:30:00+00:00  3.5300
2021-12-28 07:00:00+00:00  3.1711
2021-12-28 07:30:00+00:00  3.5300
2021-12-28 08:00:00+00:00  3.1532
2021-12-28 08:30:00+00:00  3.5100
2021-12-28 09:00:00+00:00  3.1532
2021-12-28 09:30:00+00:00  3.5100
2021-12-28 10:00:00+00:00  3.1443
2021-12-28 10:30:00+00:00  3.5100
2021-12-28 11:00:00+00:00  3.1532
2021-12-28 11:30:00+00:00  3.5100
2021-12-28 12:00:00+00:00  3.1532
2021-12-28 12:30:00+00:00  3.5100
2021-12-28 13:00:00+00:00  3.1353
2021-12-28 13:30:00+00:00  3.4400
2021-12-28 14:00:00+00:00  3.0816
2021-12-28 15:00:00+00:00  3.0816
2021-12-29 06:00:00+00:00  3.0726
2021-12-29 06:30:00+00:00  3.4100
2021-12-29 07:00:00+00:00  3.0547
2021-12-29 07:30:00+00:00  3.4700
2021-12-29 08:00:00+00:00  3.0994
2021-12-29 08:30:00+00:00  3.4400
2021-12-29 09:00:00+00:00  3.0905
2021-12-29 09:30:00+00:00  3.4500
2021-12-29 10:00:00+00:00  3.0816
2021-12-29 10:30:00+00:00  3.4300
2021-12-29 11:00:00+00:00  3.0905
2021-12-29 11:30:00+00:00  3.4700
2021-12-29 12:00:00+00:00  3.0994
2021-12-29 12:30:00+00:00  3.4800
                               price
2023-09-21 10:00:00+00:00  16.830000
2023-09-21 10:30:00+00:00  16.559999
2023-09-21 11:00:00+00:00  16.770000
2023-09-21 11:30:00+00:00  16.760000
2023-09-21 12:00:00+00:00  16.850000
2023-09-21 12:30:00+00:00  16.910000
2023-09-21 13:00:00+00:00  16.920000
2023-09-21 13:30:00+00:00  16.850000
2023-09-21 14:00:00+00:00  16.960000
2023-09-21 14:30:00+00:00  16.959999
2023-09-21 15:00:00+00:00  17.000000
2023-09-22 06:00:00+00:00  17.000000
2023-09-22 06:30:00+00:00  16.969999
2023-09-22 07:00:00+00:00  16.900000
2023-09-22 07:30:00+00:00  17.040001
2023-09-22 08:00:00+00:00  17.010000
2023-09-22 08:30:00+00:00  17.000000
2023-09-22 09:00:00+00:00  16.980000
2023-09-22 09:30:00+00:00  17.010000
2023-09-22 10:00:00+00:00  17.060000
2023-09-22 10:30:00+00:00  17.030001
2023-09-22 11:00:00+00:00  16.910000
2023-09-22 11:30:00+00:00  16.879999
2023-09-22 12:00:00+00:00  16.930000
2023-09-22 12:30:00+00:00  17.059999
2023-09-22 13:00:00+00:00  16.990000
2023-09-22 13:30:00+00:00  16.930000
2023-09-22 14:00:00+00:00  16.810000
2023-09-22 14:30:00+00:00  16.809999
2023-09-22 15:00:00+00:00  16.800000
Mean of the first 10 values: price    17.015
dtype: float64
                           price
timestamp                       
2023-09-25 09:00:00+03:00  16.89
2023-09-25 10:00:00+03:00  16.90
2023-09-25 11:00:00+03:00  16.91
2023-09-25 12:00:00+03:00  17.02
2023-09-25 13:00:00+03:00  17.09
2023-09-25 14:00:00+03:00  17.02
2023-09-25 15:00:00+03:00  17.02
2023-09-25 16:00:00+03:00  17.12
2023-09-25 17:00:00+03:00  17.08
2023-09-25 18:00:00+03:00  17.10
                                  price
2018-01-02 10:00:00+03:00  1.690000e-02
2018-01-02 11:00:00+03:00  1.690000e-02
2018-01-02 12:00:00+03:00  0.000000e+00
2018-01-02 13:00:00+03:00  0.000000e+00
2018-01-02 14:00:00+03:00  1.670000e-02
...                                 ...
2023-09-22 13:00:00+00:00 -6.999947e-02
2023-09-22 13:30:00+00:00 -5.999969e-02
2023-09-22 14:00:00+00:00 -1.200003e-01
2023-09-22 14:30:00+00:00 -5.340576e-07
2023-09-22 15:00:00+00:00 -9.999466e-03

[17888 rows x 1 columns]
ADF Statistic: -20.78216857876311
p-value: 0.0
Critical Values: {'1%': -3.4307165232547536, '5%': -2.8617019893187847, '10%': -2.5668562226754013}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                17889
Model:                ARIMA(30, 1, 1)   Log Likelihood               14652.148
Date:                Sun, 24 Dec 2023   AIC                         -29240.297
Time:                        22:41:01   BIC                         -28990.957
Sample:                             0   HQIC                        -29158.277
                              - 17889                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.4517      0.145     -3.114      0.002      -0.736      -0.167
ar.L2          0.2383      0.120      1.989      0.047       0.003       0.473
ar.L3         -0.0276      0.011     -2.464      0.014      -0.050      -0.006
ar.L4          0.0131      0.010      1.370      0.171      -0.006       0.032
ar.L5         -0.0342      0.005     -6.442      0.000      -0.045      -0.024
ar.L6          0.0386      0.008      4.998      0.000       0.023       0.054
ar.L7         -0.0127      0.007     -1.785      0.074      -0.027       0.001
ar.L8          0.0415      0.007      6.113      0.000       0.028       0.055
ar.L9         -0.0272      0.009     -2.964      0.003      -0.045      -0.009
ar.L10         0.0251      0.007      3.530      0.000       0.011       0.039
ar.L11        -0.0027      0.007     -0.402      0.688      -0.016       0.010
ar.L12        -0.0361      0.006     -6.083      0.000      -0.048      -0.024
ar.L13        -0.0214      0.007     -3.283      0.001      -0.034      -0.009
ar.L14        -0.0445      0.007     -6.813      0.000      -0.057      -0.032
ar.L15        -0.0120      0.010     -1.257      0.209      -0.031       0.007
ar.L16        -0.1629      0.006    -27.067      0.000      -0.175      -0.151
ar.L17        -0.0395      0.026     -1.528      0.126      -0.090       0.011
ar.L18         0.4268      0.015     27.544      0.000       0.396       0.457
ar.L19         0.3250      0.056      5.783      0.000       0.215       0.435
ar.L20        -0.2698      0.068     -3.962      0.000      -0.403      -0.136
ar.L21        -0.1090      0.014     -7.533      0.000      -0.137      -0.081
ar.L22         0.0075      0.022      0.347      0.729      -0.035       0.050
ar.L23        -0.0508      0.009     -5.814      0.000      -0.068      -0.034
ar.L24         0.0006      0.012      0.053      0.958      -0.022       0.023
ar.L25        -0.0517      0.007     -7.509      0.000      -0.065      -0.038
ar.L26         0.0098      0.011      0.910      0.363      -0.011       0.031
ar.L27        -0.0292      0.007     -4.238      0.000      -0.043      -0.016
ar.L28         0.0197      0.009      2.245      0.025       0.003       0.037
ar.L29        -0.0621      0.006     -9.753      0.000      -0.075      -0.050
ar.L30         0.0601      0.011      5.436      0.000       0.038       0.082
ma.L1         -0.3718      0.145     -2.565      0.010      -0.656      -0.088
sigma2         0.0114   4.64e-05    244.818      0.000       0.011       0.011
===================================================================================
Ljung-Box (L1) (Q):                   0.17   Jarque-Bera (JB):            287650.55
Prob(Q):                              0.68   Prob(JB):                         0.00
Heteroskedasticity (H):              61.74   Skew:                            -0.71
Prob(H) (two-sided):                  0.00   Kurtosis:                        22.59
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
17889    16.805598
17890    16.713067
17891    16.826916
17892    16.791374
17893    16.802531
17894    16.752224
17895    16.809814
17896    16.798400
17897    16.843114
17898    16.723802
Name: predicted_mean, dtype: float64
       lower price  upper price
17889    16.596626    17.014571
17890    16.500866    16.925267
17891    16.558479    17.095352
17892    16.516308    17.066441
17893    16.491568    17.113493
17894    16.433728    17.070721
17895    16.461090    17.158538
17896    16.442023    17.154778
17897    16.458590    17.227638
17898    16.332661    17.114943

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

Root Mean Squared Error (RMSE): 0.2451176952008188
Weighted Mean Absolute Percentage Error (WMAPE): 1.3418506846792075
In [16]:
def forecastplusyahoo(name,p,d,q):
    
    global forecast_values_dict  
    
    train1 = pd.concat([data17, data18, data19, data20, data21, data22, data23])
    test = data24

    hourly_data = train1[train1['short_name'] == name]
    print(hourly_data)
    company_tickers = [f'{name}.IS']  # Add more ticker symbols as needed

    # Fetching data for each company in the list
    for ticker_symbol in company_tickers:
        half_hourly_data = yf.download(ticker_symbol, start='2021-12-27', end='2023-09-24', interval='1h')
        #print(f"Stock data for {ticker_symbol}:\n{stock_data}\n")  # Display or save the retrieved stock data

    # Extract 'Close' column from half-hourly data
    hourly_price = hourly_data['price']
    half_hourly_close = half_hourly_data['Close']


    # Combine the close values from half-hourly data with hourly data
    combined_data = pd.concat([hourly_price, half_hourly_close], axis=1)
    combined_data.columns = ['price', 'Close']  # Rename columns for clarity

    # Update 'price' with 'Close' values where 'Close' is not NaN
    combined_data['price'] = combined_data['Close'].where(combined_data['Close'].notnull(), combined_data['price'])
    combined_data.drop(columns='Close', inplace=True)

    # Print the combined data
    print(combined_data.head(50))

    train2 = pd.concat([data1, data2, data3, data4, data5, data6, data7, data8, data9, data10, data11, data12, data13, data14, data15, data16])

    hourly2 = train2[train2['short_name'] == name]
    hourly2 = hourly2.drop(columns=['short_name'])

    company_2_data = pd.concat([hourly2, combined_data])

    print(company_2_data.tail(30))

    company_2_datat = test[test['short_name'] == name]
    company_2_datat = company_2_datat.drop(columns=['short_name'])
    company_2_datatest = company_2_datat
    mean_first_10 = company_2_datatest.head(10).mean()
    print("Mean of the first 10 values:", mean_first_10)

    print(company_2_datatest.head(10))
    from statsmodels.tsa.stattools import adfuller
    ##stationary yapma

    company_2_stationary = company_2_data.diff().dropna()
    print(company_2_stationary)

    # Assuming 'data' is your time series data
    resultxx = adfuller(company_2_stationary)
    print('ADF Statistic:', resultxx[0])
    print('p-value:', resultxx[1])
    print('Critical Values:', resultxx[4])

    # stationary check, it is stationary(ADF lower than critical values and p-value close to 0)

    model = ARIMA(company_2_data, order=(p, q, d))
    results = model.fit()
    print(results.summary())

    forecast = results.get_prediction(start=len(company_2_data), end=len(company_2_data) + 9)
    mean_forecast = forecast.predicted_mean
    confidence_intervals = forecast.conf_int()
    lower_limits = confidence_intervals.loc[:, 'lower price']
    upper_limits = confidence_intervals.loc[:, 'upper price']

    print(mean_forecast)
    print(confidence_intervals)

    # Assuming 'lower_limits' and 'upper_limits' contain the corresponding confidence intervals
    # Assuming 'company_1_data2' contains your second dataset

    plt.figure()

    # Plotting the forecasted values and confidence intervals
    plt.plot(mean_forecast.values, color='red', label='Forecast')
    plt.fill_between(range(len(mean_forecast)), lower_limits, upper_limits, color='pink', alpha=0.3)

    # Plotting the first 25 points of company_1_data2 as 'Actual Data'
    plt.plot(range(10), company_2_datatest.iloc[:10], label='Actual Data', marker='o')  # Adjust the slicing as needed

    plt.xlabel('Index')  # Update with appropriate label
    plt.ylabel('Values')  # Update with appropriate label
    plt.title('Comparison between Forecast and Actual Data')
    plt.legend()
    plt.show()

    from sklearn.metrics import mean_squared_error
    import numpy as np

    # Assuming 'actual_data' contains your actual data and 'mean_forecast' contains the mean forecast
    # Convert 'actual_data' and 'mean_forecast' to numpy arrays if they are not already
    actual_values = np.array(company_2_datatest.iloc[:10])
    forecast_values = np.array(mean_forecast)

    # Calculate RMSE
    rmse = np.sqrt(mean_squared_error(actual_values, forecast_values))
    print(f"Root Mean Squared Error (RMSE): {rmse}")
    
    # Calculate WMAPE
    weights = np.ones(len(actual_values))  # You can assign different weights if needed
    wmape = (np.sum(weights * np.abs(actual_values - forecast_values)) / np.sum(weights * actual_values)) * 100
    print(f"Weighted Mean Absolute Percentage Error (WMAPE): {wmape}")
    
    forecast_values_dict[name] = forecast_values
In [751]:
forecast_values_dict
Out[751]:
{'DOHOL': array([13.01306449, 13.04257918, 13.02973437, 13.07453275, 13.02937603,
        13.05913917, 13.04881921, 13.082186  , 13.03374603, 13.00613119]),
 'THYAO': array([226.13101751, 226.26852833, 226.42032994, 226.6087945 ,
        226.70787386, 226.59144776, 226.55427092, 226.54366528,
        226.41961549, 226.23183637]),
 'AKBNK': array([30.80978808, 30.63629036, 30.88432712, 30.75440825, 30.75042825,
        30.68261854, 30.87246131, 30.82266251, 30.87164805, 30.64957291]),
 'ARCLK': array([155.06442848, 155.4578826 , 154.13914292, 156.07072694,
        153.92670687, 156.1784913 , 153.63655976, 156.44945324,
        153.92343616, 156.39371036]),
 'ASELS': array([40.62093168, 40.72080647, 40.7926436 , 40.8386505 , 40.84498468,
        40.92750797, 40.99724979, 41.0857884 , 41.16063593, 41.22911896]),
 'BIMAS': array([273.54756357, 272.9005355 , 272.51219702, 272.84021062,
        273.0163417 , 273.29856892, 272.83809248, 272.99803782,
        273.1449719 , 272.82298172]),
 'EKGYO': array([7.90707046, 7.90059903, 7.90056802, 7.92245672, 7.91757176,
        7.93844774, 7.92907451, 7.94344171, 7.93026525, 7.92601819]),
 'EREGL': array([43.42271402, 43.67570074, 43.68156492, 43.88839659, 44.03190447,
        44.1858422 , 44.04997057, 44.14717663, 44.82232925, 44.70765931]),
 'FROTO': array([830.99342011, 831.41658266, 816.41586429, 836.6674633 ,
        815.88834072, 839.91271182, 814.74433573, 837.25802376,
        815.20918087, 835.08460046]),
 'GUBRF': array([339.87796362, 340.16697833, 340.50847152, 340.84338934,
        340.99136619, 341.17266403, 341.26792064, 341.42562748,
        341.55386215, 341.53078252]),
 'GARAN': array([50.83791303, 50.47171095, 50.86708609, 50.64472572, 50.75083697,
        50.60956213, 50.88829045, 50.82087524, 50.90530508, 50.53815156]),
 'KRDMD': array([27.721452  , 27.77772753, 27.79666637, 28.00045428, 28.02067117,
        28.04435518, 27.95967038, 28.07780282, 28.48802435, 28.51923712]),
 'KCHOL': array([137.70920812, 137.78882709, 137.60628609, 137.50293096,
        137.49009867, 137.91650468, 137.45401574, 137.74890979,
        137.48307846, 137.51370898]),
 'KOZAL': array([28.15296182, 27.81152165, 28.01582755, 27.85912797, 27.92805541,
        27.79629562, 27.850601  , 27.810418  , 27.88405013, 27.75427053]),
 'KOZAA': array([64.442217  , 64.49914624, 64.61765373, 64.63157838, 64.57373582,
        64.53594664, 64.5626773 , 64.51316425, 64.49992953, 64.43331413]),
 'PGSUS': array([767.56774435, 767.897003  , 768.60457119, 769.65587515,
        770.17994418, 770.11791094, 770.48379815, 770.86778067,
        770.69242384, 770.56229437]),
 'PETKM': array([19.94173525, 19.97642075, 20.02653009, 20.0333102 , 20.07217744,
        20.07708441, 20.13076748, 20.12609185, 20.19109692, 20.17880116]),
 'SAHOL': array([56.56999413, 56.33595434, 56.33811775, 56.22998732, 56.2911494 ,
        56.30971749, 56.22538251, 56.30170441, 56.16669052, 55.98690546]),
 'SASA': array([45.47200588, 45.50652578, 45.44123154, 45.47844539, 45.41828789,
        45.38917362, 45.34201528, 45.31660873, 45.26293691, 45.29011549]),
 'SISE': array([54.65009571, 54.56645649, 54.49723824, 54.5159781 , 54.45040415,
        54.51558208, 54.51294552, 54.67361163, 54.60204021, 54.59185697]),
 'TAVHL': array([119.2118943 , 119.37612117, 119.42077278, 119.54144124,
        119.46942857, 119.3852175 , 119.3559883 , 119.24559832,
        119.24620871, 119.14375031]),
 'TKFEN': array([53.08337418, 51.29917802, 52.16497887, 51.49742949, 51.80733827,
        51.44371168, 51.71235968, 51.66294665, 51.96932133, 51.47331446]),
 'TUPRS': array([157.13331746, 156.27664193, 152.93421588, 155.7690737 ,
        152.24444009, 156.11603578, 151.89893673, 154.61978833,
        150.4155922 , 153.22341825]),
 'TTKOM': array([23.11897847, 23.14119281, 23.30755215, 23.29280935, 23.2157269 ,
        23.22841065, 23.16826001, 23.23829154, 23.20723445, 23.17652782]),
 'TCELL': array([54.35313425, 54.42334094, 54.415687  , 54.42949023, 54.41160504,
        54.44819581, 54.34379083, 54.44854446, 54.34923009, 54.43303394]),
 'HALKB': array([15.25031614, 15.19856817, 15.20964401, 15.20120812, 15.19495896,
        15.19516409, 15.17884975, 15.16737502, 15.16180558, 15.14571866]),
 'ISCTR': array([24.92934268, 24.77714168, 25.08263182, 25.00058133, 25.02540662,
        24.82405421, 25.01017606, 25.19772373, 25.34944429, 25.09607709]),
 'VAKBN': array([13.50670414, 13.49999039, 13.4996114 , 13.5083786 , 13.50868853,
        13.50496283, 13.50066606, 13.50330453, 13.51232156, 13.50727618]),
 'VESTL': array([64.12090282, 64.06530507, 64.03466852, 64.08277756, 64.10047854,
        64.04165974, 64.00051075, 63.99428248, 64.05127422, 64.03409011]),
 'YKBNK': array([16.80559848, 16.71306661, 16.82691555, 16.79137443, 16.80253076,
        16.75222425, 16.80981406, 16.7984003 , 16.84311436, 16.72380225])}

Next, I have updated the up to date data in submission stage. I have made an another function since there is no test data now. Also, the yahoo finance data in the function code is updated every day since it can only give the last 2 years of data. I have also changed the parameters of those companies where the model overpredicted to 2,1,1 here.

In [3]:
#UP TO DATE DATA

import pandas as pd
data24= pd.read_csv('20230925_20231120_bist30.csv', index_col='timestamp', parse_dates=True)
data25=pd.read_csv('20231121_20231209_bist30.csv', index_col='timestamp', parse_dates=True)
data26= pd.read_csv('Daily_Series - 20231210_20240111.csv', index_col='timestamp', parse_dates=True)
print(data25.tail(50))
submissions= {}
 

                           price short_name
timestamp                                  
2023-12-04 09:00:00+03:00  19.83      YKBNK
2023-12-04 10:00:00+03:00  19.82      YKBNK
2023-12-04 11:00:00+03:00  19.78      YKBNK
2023-12-04 12:00:00+03:00  19.86      YKBNK
2023-12-04 13:00:00+03:00  19.78      YKBNK
2023-12-04 14:00:00+03:00  19.83      YKBNK
2023-12-04 15:00:00+03:00  19.90      YKBNK
2023-12-04 16:00:00+03:00  20.22      YKBNK
2023-12-04 17:00:00+03:00  20.50      YKBNK
2023-12-04 18:00:00+03:00  20.52      YKBNK
2023-12-05 09:00:00+03:00  20.42      YKBNK
2023-12-05 10:00:00+03:00  20.40      YKBNK
2023-12-05 11:00:00+03:00  20.34      YKBNK
2023-12-05 12:00:00+03:00  20.48      YKBNK
2023-12-05 13:00:00+03:00  20.32      YKBNK
2023-12-05 14:00:00+03:00  20.40      YKBNK
2023-12-05 15:00:00+03:00  20.32      YKBNK
2023-12-05 16:00:00+03:00  20.30      YKBNK
2023-12-05 17:00:00+03:00  20.24      YKBNK
2023-12-05 18:00:00+03:00  20.28      YKBNK
2023-12-06 09:00:00+03:00  20.28      YKBNK
2023-12-06 10:00:00+03:00  20.82      YKBNK
2023-12-06 11:00:00+03:00  20.64      YKBNK
2023-12-06 12:00:00+03:00  20.50      YKBNK
2023-12-06 13:00:00+03:00  20.60      YKBNK
2023-12-06 14:00:00+03:00  20.36      YKBNK
2023-12-06 15:00:00+03:00  20.56      YKBNK
2023-12-06 16:00:00+03:00  20.62      YKBNK
2023-12-06 17:00:00+03:00  20.50      YKBNK
2023-12-06 18:00:00+03:00  20.42      YKBNK
2023-12-07 09:00:00+03:00  20.26      YKBNK
2023-12-07 10:00:00+03:00  19.79      YKBNK
2023-12-07 11:00:00+03:00  19.54      YKBNK
2023-12-07 12:00:00+03:00  19.47      YKBNK
2023-12-07 13:00:00+03:00  19.56      YKBNK
2023-12-07 14:00:00+03:00  19.43      YKBNK
2023-12-07 15:00:00+03:00  19.40      YKBNK
2023-12-07 16:00:00+03:00  19.50      YKBNK
2023-12-07 17:00:00+03:00  19.76      YKBNK
2023-12-07 18:00:00+03:00  19.89      YKBNK
2023-12-08 09:00:00+03:00  19.95      YKBNK
2023-12-08 10:00:00+03:00  19.83      YKBNK
2023-12-08 11:00:00+03:00  19.88      YKBNK
2023-12-08 12:00:00+03:00  19.72      YKBNK
2023-12-08 13:00:00+03:00  19.69      YKBNK
2023-12-08 14:00:00+03:00  19.72      YKBNK
2023-12-08 15:00:00+03:00  19.74      YKBNK
2023-12-08 16:00:00+03:00  19.81      YKBNK
2023-12-08 17:00:00+03:00  19.67      YKBNK
2023-12-08 18:00:00+03:00  19.67      YKBNK
In [4]:
## updating the code everyday (data26 changes every day, and yahoofinance data can only have 2 years of data so updating its date is also necessary)

def forecastfinal(name,p,d,q):
    
    global submissions
    
    train1 = pd.concat([data17, data18, data19, data20, data21, data22, data23, data24,data25,data26])
  

    hourly_data = train1[train1['short_name'] == name]
    print(hourly_data)
    company_tickers = [f'{name}.IS']  # Add more ticker symbols as needed

    # Fetching data for each company in the list
    for ticker_symbol in company_tickers:
        half_hourly_data = yf.download(ticker_symbol, start='2022-01-12', end='2024-01-12', interval='1h')
          # Display or save the retrieved stock data

    # Extract 'Close' column from half-hourly data
    hourly_price = hourly_data['price']
    half_hourly_close = half_hourly_data['Close']
    
    ######
    #UPDATING NEW DATA EVERYDAY!!!
    ###########
    
   # new=yf.download(f'{name}.IS', start='2023-12-23', end='2023-12-26', interval='1h')
    #new=new['Adj Close']
    
    
    
    # Combine the close values from half-hourly data with hourly data
    combined_data = pd.concat([hourly_price, half_hourly_close], axis=1)
   

    # Update 'price' with 'Close' values where 'Close' is not NaN
    combined_data['price'] = combined_data['Close'].where(combined_data['Close'].notnull(), combined_data['price'])
    #combined_data['price'] = combined_data['Adj Close'].where(combined_data['Adj Close'].notnull(), combined_data['price'])
    combined_data=combined_data['price']

    # Print the combined data
    print(combined_data.head(50))

    train2 = pd.concat([data1, data2, data3, data4, data5, data6, data7, data8, data9, data10, data11, data12, data13, data14, data15, data16])

    hourly2 = train2[train2['short_name'] == name]
    hourly2 = hourly2['price']
    
   

    company_2_data = pd.concat([hourly2, combined_data])

  
    print(company_2_data.tail(30))


    from statsmodels.tsa.stattools import adfuller
    ##stationary yapma

    company_2_stationary = company_2_data.diff().dropna()
    print(company_2_stationary)

    # Assuming 'data' is your time series data
    resultxx = adfuller(company_2_stationary)
    print('ADF Statistic:', resultxx[0])
    print('p-value:', resultxx[1])
    print('Critical Values:', resultxx[4])

    # stationary check, it is stationary(ADF lower than critical values and p-value close to 0)

    model = ARIMA(company_2_data, order=(p, q, d))
    results = model.fit()
    print(results.summary())

    forecast = results.get_prediction(start=len(company_2_data), end=len(company_2_data) + 9)
    mean_forecast = forecast.predicted_mean
    confidence_intervals = forecast.conf_int()
    lower_limits = confidence_intervals.loc[:, 'lower price']
    upper_limits = confidence_intervals.loc[:, 'upper price']

    forecast_values = np.array(mean_forecast)
    
    print(mean_forecast)
    print(confidence_intervals)

    # Assuming 'lower_limits' and 'upper_limits' contain the corresponding confidence intervals
    # Assuming 'company_1_data2' contains your second dataset

    
    
    submissions[name] = forecast_values
In [5]:
forecastfinal('THYAO', 30, 1, 3)
forecastfinal('AKBNK', 30, 1, 1)
forecastfinal('ARCLK', 30, 1, 1)
forecastfinal('ASELS', 30, 1, 3)
forecastfinal('BIMAS', 30, 1, 1)
forecastfinal('DOHOL', 2, 1, 1)
forecastfinal('EKGYO', 2, 1, 1)
forecastfinal('EREGL', 30, 1, 3)
forecastfinal('FROTO', 30, 1, 1)
forecastfinal('GUBRF', 30, 1, 3)
forecastfinal('GARAN', 30, 1, 1)
forecastfinal('KRDMD', 30, 1, 3)
forecastfinal('KCHOL', 30, 1, 3)
forecastfinal('KOZAL', 30, 1, 1)
forecastfinal('KOZAA', 30, 1, 3)
forecastfinal('PGSUS', 30, 1, 3)
forecastfinal('PETKM', 30, 1, 3)
forecastfinal('SAHOL', 30, 1, 1)
forecastfinal('SASA', 30, 1, 1)
forecastfinal('SISE', 30, 1, 1)
forecastfinal('TAVHL', 30, 1, 3)
forecastfinal('TKFEN', 30, 1, 1)
forecastfinal('TUPRS', 30, 1, 3)
forecastfinal('TTKOM', 30, 1, 3)
forecastfinal('TCELL', 30, 1, 1)
forecastfinal('HALKB', 30, 1, 1)
forecastfinal('ISCTR', 30, 1, 1)
forecastfinal('VAKBN', 30, 1, 1)
forecastfinal('VESTL', 30, 1, 1)
forecastfinal('YKBNK', 30, 1, 1)

                            price short_name
timestamp                                   
2021-12-27 09:00:00+03:00   21.04      THYAO
2021-12-27 10:00:00+03:00   21.54      THYAO
2021-12-27 11:00:00+03:00   21.20      THYAO
2021-12-27 12:00:00+03:00   21.18      THYAO
2021-12-27 13:00:00+03:00   21.14      THYAO
...                           ...        ...
2024-01-11 14:00:00+03:00  247.30      THYAO
2024-01-11 15:00:00+03:00  245.40      THYAO
2024-01-11 16:00:00+03:00  246.20      THYAO
2024-01-11 17:00:00+03:00  246.70      THYAO
2024-01-11 18:00:00+03:00  246.70      THYAO

[5104 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    21.04
2021-12-27 07:00:00+00:00    21.54
2021-12-27 08:00:00+00:00    21.20
2021-12-27 09:00:00+00:00    21.18
2021-12-27 10:00:00+00:00    21.14
2021-12-27 11:00:00+00:00    20.88
2021-12-27 12:00:00+00:00    20.86
2021-12-27 13:00:00+00:00    21.04
2021-12-27 14:00:00+00:00    20.78
2021-12-27 15:00:00+00:00    20.78
2021-12-28 06:00:00+00:00    20.86
2021-12-28 07:00:00+00:00    21.06
2021-12-28 08:00:00+00:00    20.94
2021-12-28 09:00:00+00:00    20.98
2021-12-28 10:00:00+00:00    20.92
2021-12-28 11:00:00+00:00    20.78
2021-12-28 12:00:00+00:00    20.78
2021-12-28 13:00:00+00:00    20.48
2021-12-28 14:00:00+00:00    20.60
2021-12-28 15:00:00+00:00    20.48
2021-12-29 06:00:00+00:00    20.08
2021-12-29 07:00:00+00:00    20.04
2021-12-29 08:00:00+00:00    20.70
2021-12-29 09:00:00+00:00    20.88
2021-12-29 10:00:00+00:00    20.86
2021-12-29 11:00:00+00:00    20.94
2021-12-29 12:00:00+00:00    21.10
2021-12-29 13:00:00+00:00    21.52
2021-12-29 14:00:00+00:00    21.20
2021-12-29 15:00:00+00:00    21.08
2021-12-30 06:00:00+00:00    21.42
2021-12-30 07:00:00+00:00    21.18
2021-12-30 08:00:00+00:00    21.10
2021-12-30 09:00:00+00:00    20.72
2021-12-30 10:00:00+00:00    20.56
2021-12-30 11:00:00+00:00    20.22
2021-12-30 12:00:00+00:00    20.36
2021-12-30 13:00:00+00:00    20.50
2021-12-30 14:00:00+00:00    20.30
2021-12-30 15:00:00+00:00    20.14
2021-12-31 06:00:00+00:00    20.30
2021-12-31 07:00:00+00:00    20.68
2021-12-31 08:00:00+00:00    20.70
2021-12-31 09:00:00+00:00    20.94
2021-12-31 10:00:00+00:00    20.50
2021-12-31 11:00:00+00:00    20.46
2021-12-31 12:00:00+00:00    20.32
2021-12-31 13:00:00+00:00    20.28
2021-12-31 14:00:00+00:00    19.98
2021-12-31 15:00:00+00:00    20.02
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    246.000000
2024-01-10 10:00:00+00:00    248.200000
2024-01-10 10:30:00+00:00    247.699997
2024-01-10 11:00:00+00:00    247.900000
2024-01-10 11:30:00+00:00    249.100006
2024-01-10 12:00:00+00:00    248.400000
2024-01-10 12:30:00+00:00    249.500000
2024-01-10 13:00:00+00:00    249.800000
2024-01-10 13:30:00+00:00    250.500000
2024-01-10 14:00:00+00:00    250.500000
2024-01-10 15:00:00+00:00    249.900000
2024-01-11 06:00:00+00:00    250.500000
2024-01-11 06:30:00+00:00    249.100006
2024-01-11 07:00:00+00:00    246.800000
2024-01-11 07:30:00+00:00    246.800003
2024-01-11 08:00:00+00:00    246.400000
2024-01-11 08:30:00+00:00    247.699997
2024-01-11 09:00:00+00:00    247.800000
2024-01-11 09:30:00+00:00    248.000000
2024-01-11 10:00:00+00:00    247.400000
2024-01-11 10:30:00+00:00    247.000000
2024-01-11 11:00:00+00:00    247.300000
2024-01-11 11:30:00+00:00    246.300003
2024-01-11 12:00:00+00:00    245.400000
2024-01-11 12:30:00+00:00    247.699997
2024-01-11 13:00:00+00:00    246.200000
2024-01-11 13:30:00+00:00    245.699997
2024-01-11 14:00:00+00:00    246.700000
2024-01-11 14:30:00+00:00    246.699997
2024-01-11 15:00:00+00:00    246.700000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    0.220000
2018-01-02 11:00:00+03:00    0.040000
2018-01-02 12:00:00+03:00    0.000000
2018-01-02 13:00:00+03:00    0.010000
2018-01-02 14:00:00+03:00   -0.010000
                               ...   
2024-01-11 13:00:00+00:00   -1.499997
2024-01-11 13:30:00+00:00   -0.500003
2024-01-11 14:00:00+00:00    1.000003
2024-01-11 14:30:00+00:00   -0.000003
2024-01-11 15:00:00+00:00    0.000003
Name: price, Length: 19222, dtype: float64
ADF Statistic: -27.880607006689658
p-value: 0.0
Critical Values: {'1%': -3.430690705077546, '5%': -2.8616905793225387, '10%': -2.566850149319263}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19223
Model:                ARIMA(30, 3, 1)   Log Likelihood              -18009.821
Date:                Thu, 11 Jan 2024   AIC                          36083.642
Time:                        21:10:52   BIC                          36335.280
Sample:                             0   HQIC                         36166.129
                              - 19223                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.8990      0.003   -320.736      0.000      -0.904      -0.893
ar.L2         -0.8762      0.004   -213.244      0.000      -0.884      -0.868
ar.L3         -0.8496      0.006   -145.051      0.000      -0.861      -0.838
ar.L4         -0.8169      0.007   -116.546      0.000      -0.831      -0.803
ar.L5         -0.7846      0.008   -101.223      0.000      -0.800      -0.769
ar.L6         -0.7351      0.008    -87.672      0.000      -0.752      -0.719
ar.L7         -0.6982      0.009    -77.136      0.000      -0.716      -0.680
ar.L8         -0.6801      0.010    -70.938      0.000      -0.699      -0.661
ar.L9         -0.6448      0.010    -65.053      0.000      -0.664      -0.625
ar.L10        -0.5969      0.010    -60.564      0.000      -0.616      -0.578
ar.L11        -0.5554      0.010    -55.306      0.000      -0.575      -0.536
ar.L12        -0.5262      0.010    -50.818      0.000      -0.547      -0.506
ar.L13        -0.4867      0.011    -46.225      0.000      -0.507      -0.466
ar.L14        -0.4488      0.011    -41.602      0.000      -0.470      -0.428
ar.L15        -0.4353      0.011    -40.123      0.000      -0.457      -0.414
ar.L16        -0.3978      0.011    -37.321      0.000      -0.419      -0.377
ar.L17        -0.3511      0.011    -32.970      0.000      -0.372      -0.330
ar.L18        -0.3289      0.011    -31.304      0.000      -0.349      -0.308
ar.L19        -0.3197      0.010    -31.029      0.000      -0.340      -0.300
ar.L20        -0.3330      0.010    -32.893      0.000      -0.353      -0.313
ar.L21        -0.3292      0.010    -32.179      0.000      -0.349      -0.309
ar.L22        -0.3219      0.010    -33.654      0.000      -0.341      -0.303
ar.L23        -0.2970      0.010    -30.809      0.000      -0.316      -0.278
ar.L24        -0.2847      0.010    -29.730      0.000      -0.303      -0.266
ar.L25        -0.2170      0.009    -24.340      0.000      -0.234      -0.200
ar.L26        -0.1617      0.008    -19.120      0.000      -0.178      -0.145
ar.L27        -0.1356      0.008    -17.658      0.000      -0.151      -0.121
ar.L28        -0.1006      0.007    -14.905      0.000      -0.114      -0.087
ar.L29        -0.0745      0.006    -13.437      0.000      -0.085      -0.064
ar.L30        -0.0371      0.004     -9.340      0.000      -0.045      -0.029
ma.L1         -1.0000      0.006   -180.533      0.000      -1.011      -0.989
sigma2         0.3811      0.002    195.105      0.000       0.377       0.385
===================================================================================
Ljung-Box (L1) (Q):                   0.03   Jarque-Bera (JB):           1860122.24
Prob(Q):                              0.87   Prob(JB):                         0.00
Heteroskedasticity (H):              78.71   Skew:                             2.22
Prob(H) (two-sided):                  0.00   Kurtosis:                        50.99
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223    246.728447
19224    246.787834
19225    246.901565
19226    247.047472
19227    247.219273
19228    247.206833
19229    247.091589
19230    247.069706
19231    246.883991
19232    246.690849
Name: predicted_mean, dtype: float64
       lower price  upper price
19223   245.518519   247.938375
19224   244.988161   248.587507
19225   244.638452   249.164677
19226   244.380381   249.714562
19227   244.179175   250.259371
19228   243.812869   250.600797
19229   243.347308   250.835871
19230   242.980262   251.159151
19231   242.461020   251.306961
19232   241.936427   251.445271

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00   6.6937      AKBNK
2021-12-27 10:00:00+03:00   6.7991      AKBNK
2021-12-27 11:00:00+03:00   6.8343      AKBNK
2021-12-27 12:00:00+03:00   6.8430      AKBNK
2021-12-27 13:00:00+03:00   6.8255      AKBNK
...                            ...        ...
2024-01-11 14:00:00+03:00  41.1200      AKBNK
2024-01-11 15:00:00+03:00  41.1600      AKBNK
2024-01-11 16:00:00+03:00  41.3200      AKBNK
2024-01-11 17:00:00+03:00  41.1200      AKBNK
2024-01-11 18:00:00+03:00  41.1200      AKBNK

[5104 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    6.6937
2021-12-27 07:00:00+00:00    6.7991
2021-12-27 08:00:00+00:00    6.8343
2021-12-27 09:00:00+00:00    6.8430
2021-12-27 10:00:00+00:00    6.8255
2021-12-27 11:00:00+00:00    6.7816
2021-12-27 12:00:00+00:00    6.7904
2021-12-27 13:00:00+00:00    6.8343
2021-12-27 14:00:00+00:00    6.8782
2021-12-27 15:00:00+00:00    6.8782
2021-12-28 06:00:00+00:00    6.8430
2021-12-28 07:00:00+00:00    6.6850
2021-12-28 08:00:00+00:00    6.6323
2021-12-28 09:00:00+00:00    6.6147
2021-12-28 10:00:00+00:00    6.5884
2021-12-28 11:00:00+00:00    6.5884
2021-12-28 12:00:00+00:00    6.6410
2021-12-28 13:00:00+00:00    6.5708
2021-12-28 14:00:00+00:00    6.4917
2021-12-28 15:00:00+00:00    6.4653
2021-12-29 06:00:00+00:00    6.3775
2021-12-29 07:00:00+00:00    6.3599
2021-12-29 08:00:00+00:00    6.4477
2021-12-29 09:00:00+00:00    6.4389
2021-12-29 10:00:00+00:00    6.4389
2021-12-29 11:00:00+00:00    6.4214
2021-12-29 12:00:00+00:00    6.4741
2021-12-29 13:00:00+00:00    6.5005
2021-12-29 14:00:00+00:00    6.4653
2021-12-29 15:00:00+00:00    6.4565
2021-12-30 06:00:00+00:00    6.4741
2021-12-30 07:00:00+00:00    6.5357
2021-12-30 08:00:00+00:00    6.5796
2021-12-30 09:00:00+00:00    6.5796
2021-12-30 10:00:00+00:00    6.5796
2021-12-30 11:00:00+00:00    6.5269
2021-12-30 12:00:00+00:00    6.5269
2021-12-30 13:00:00+00:00    6.5444
2021-12-30 14:00:00+00:00    6.4653
2021-12-30 15:00:00+00:00    6.4477
2021-12-31 06:00:00+00:00    6.4565
2021-12-31 07:00:00+00:00    6.4565
2021-12-31 08:00:00+00:00    6.5093
2021-12-31 09:00:00+00:00    6.5005
2021-12-31 10:00:00+00:00    6.4829
2021-12-31 11:00:00+00:00    6.4741
2021-12-31 12:00:00+00:00    6.4214
2021-12-31 13:00:00+00:00    6.3775
2021-12-31 14:00:00+00:00    6.2984
2021-12-31 15:00:00+00:00    6.3248
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    39.740002
2024-01-10 10:00:00+00:00    39.860000
2024-01-10 10:30:00+00:00    40.000000
2024-01-10 11:00:00+00:00    39.940000
2024-01-10 11:30:00+00:00    40.020000
2024-01-10 12:00:00+00:00    39.960000
2024-01-10 12:30:00+00:00    40.139999
2024-01-10 13:00:00+00:00    40.820000
2024-01-10 13:30:00+00:00    40.720001
2024-01-10 14:00:00+00:00    40.740000
2024-01-10 15:00:00+00:00    40.740000
2024-01-11 06:00:00+00:00    40.980000
2024-01-11 06:30:00+00:00    41.160000
2024-01-11 07:00:00+00:00    40.720000
2024-01-11 07:30:00+00:00    40.980000
2024-01-11 08:00:00+00:00    40.920000
2024-01-11 08:30:00+00:00    41.119999
2024-01-11 09:00:00+00:00    41.160000
2024-01-11 09:30:00+00:00    41.200001
2024-01-11 10:00:00+00:00    41.200000
2024-01-11 10:30:00+00:00    41.160000
2024-01-11 11:00:00+00:00    41.120000
2024-01-11 11:30:00+00:00    41.160000
2024-01-11 12:00:00+00:00    41.160000
2024-01-11 12:30:00+00:00    41.419998
2024-01-11 13:00:00+00:00    41.320000
2024-01-11 13:30:00+00:00    40.980000
2024-01-11 14:00:00+00:00    41.120000
2024-01-11 14:30:00+00:00    41.119999
2024-01-11 15:00:00+00:00    41.120000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    0.112700
2018-01-02 11:00:00+03:00    0.035200
2018-01-02 12:00:00+03:00   -0.014000
2018-01-02 13:00:00+03:00    0.021000
2018-01-02 14:00:00+03:00    0.035300
                               ...   
2024-01-11 13:00:00+00:00   -0.099998
2024-01-11 13:30:00+00:00   -0.340000
2024-01-11 14:00:00+00:00    0.140000
2024-01-11 14:30:00+00:00   -0.000001
2024-01-11 15:00:00+00:00    0.000001
Name: price, Length: 19223, dtype: float64
ADF Statistic: -21.016923841077208
p-value: 0.0
Critical Values: {'1%': -3.4306909715527216, '5%': -2.8616906970881657, '10%': -2.5668502120039105}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19224
Model:                ARIMA(30, 1, 1)   Log Likelihood                2573.790
Date:                Thu, 11 Jan 2024   AIC                          -5083.580
Time:                        21:13:47   BIC                          -4831.936
Sample:                             0   HQIC                         -5001.092
                              - 19224                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.6352      0.133     -4.779      0.000      -0.896      -0.375
ar.L2          0.0872      0.104      0.842      0.400      -0.116       0.290
ar.L3         -0.0333      0.006     -5.633      0.000      -0.045      -0.022
ar.L4          0.0203      0.008      2.654      0.008       0.005       0.035
ar.L5         -0.0371      0.006     -5.902      0.000      -0.049      -0.025
ar.L6          0.0170      0.008      2.184      0.029       0.002       0.032
ar.L7         -0.0249      0.007     -3.613      0.000      -0.038      -0.011
ar.L8          0.0181      0.008      2.314      0.021       0.003       0.033
ar.L9         -0.0222      0.008     -2.917      0.004      -0.037      -0.007
ar.L10         0.0492      0.008      6.381      0.000       0.034       0.064
ar.L11         0.0215      0.009      2.444      0.015       0.004       0.039
ar.L12        -0.0164      0.008     -2.111      0.035      -0.032      -0.001
ar.L13        -0.0098      0.006     -1.642      0.101      -0.022       0.002
ar.L14        -0.0308      0.006     -5.118      0.000      -0.043      -0.019
ar.L15        -0.0173      0.007     -2.478      0.013      -0.031      -0.004
ar.L16        -0.1657      0.005    -30.735      0.000      -0.176      -0.155
ar.L17        -0.0741      0.023     -3.240      0.001      -0.119      -0.029
ar.L18         0.3654      0.014     27.041      0.000       0.339       0.392
ar.L19         0.3634      0.047      7.790      0.000       0.272       0.455
ar.L20        -0.1933      0.055     -3.515      0.000      -0.301      -0.086
ar.L21        -0.1393      0.018     -7.599      0.000      -0.175      -0.103
ar.L22        -0.0266      0.022     -1.225      0.221      -0.069       0.016
ar.L23        -0.0559      0.008     -6.640      0.000      -0.072      -0.039
ar.L24         0.0081      0.010      0.783      0.434      -0.012       0.028
ar.L25        -0.0301      0.007     -4.586      0.000      -0.043      -0.017
ar.L26         0.0308      0.008      3.924      0.000       0.015       0.046
ar.L27        -0.0146      0.008     -1.860      0.063      -0.030       0.001
ar.L28         0.0082      0.007      1.146      0.252      -0.006       0.022
ar.L29        -0.0494      0.007     -7.296      0.000      -0.063      -0.036
ar.L30         0.0548      0.011      5.103      0.000       0.034       0.076
ma.L1         -0.1417      0.133     -1.067      0.286      -0.402       0.119
sigma2         0.0446      0.000    240.002      0.000       0.044       0.045
===================================================================================
Ljung-Box (L1) (Q):                   0.07   Jarque-Bera (JB):            242419.32
Prob(Q):                              0.79   Prob(JB):                         0.00
Heteroskedasticity (H):              30.90   Skew:                            -0.42
Prob(H) (two-sided):                  0.00   Kurtosis:                        20.38
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224    41.214986
19225    41.003356
19226    40.975632
19227    41.094561
19228    41.061683
19229    41.157111
19230    41.064339
19231    41.144549
19232    41.062722
19233    41.052960
Name: predicted_mean, dtype: float64
       lower price  upper price
19224    40.801192    41.628779
19225    40.579389    41.427323
19226    40.436767    41.514497
19227    40.538270    41.650851
19228    40.428833    41.694532
19229    40.506502    41.807721
19230    40.351493    41.777184
19231    40.415053    41.874045
19232    40.278098    41.847345
19233    40.253389    41.852530
                              price short_name
timestamp                                     
2021-12-27 09:00:00+03:00   49.8145      ARCLK
2021-12-27 10:00:00+03:00   49.6728      ARCLK
2021-12-27 11:00:00+03:00   49.4837      ARCLK
2021-12-27 12:00:00+03:00   48.9639      ARCLK
2021-12-27 13:00:00+03:00   49.3419      ARCLK
...                             ...        ...
2024-01-11 14:00:00+03:00  130.2000      ARCLK
2024-01-11 15:00:00+03:00  129.5000      ARCLK
2024-01-11 16:00:00+03:00  129.6000      ARCLK
2024-01-11 17:00:00+03:00  129.8000      ARCLK
2024-01-11 18:00:00+03:00  129.5000      ARCLK

[5104 rows x 2 columns]

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    49.8145
2021-12-27 07:00:00+00:00    49.6728
2021-12-27 08:00:00+00:00    49.4837
2021-12-27 09:00:00+00:00    48.9639
2021-12-27 10:00:00+00:00    49.3419
2021-12-27 11:00:00+00:00    48.7747
2021-12-27 12:00:00+00:00    48.1131
2021-12-27 13:00:00+00:00    48.3022
2021-12-27 14:00:00+00:00    48.3494
2021-12-27 15:00:00+00:00    48.1131
2021-12-28 06:00:00+00:00    48.4439
2021-12-28 07:00:00+00:00    48.4439
2021-12-28 08:00:00+00:00    48.6803
2021-12-28 09:00:00+00:00    48.4439
2021-12-28 10:00:00+00:00    48.1131
2021-12-28 11:00:00+00:00    47.7350
2021-12-28 12:00:00+00:00    47.4987
2021-12-28 13:00:00+00:00    47.3569
2021-12-28 14:00:00+00:00    46.7898
2021-12-28 15:00:00+00:00    46.8276
2021-12-29 06:00:00+00:00    46.5062
2021-12-29 07:00:00+00:00    46.5062
2021-12-29 08:00:00+00:00    47.4987
2021-12-29 09:00:00+00:00    47.4041
2021-12-29 10:00:00+00:00    47.3569
2021-12-29 11:00:00+00:00    47.4041
2021-12-29 12:00:00+00:00    47.4041
2021-12-29 13:00:00+00:00    47.4041
2021-12-29 14:00:00+00:00    47.3097
2021-12-29 15:00:00+00:00    47.4514
2021-12-30 06:00:00+00:00    47.6404
2021-12-30 07:00:00+00:00    47.3097
2021-12-30 08:00:00+00:00    47.4514
2021-12-30 09:00:00+00:00    47.2624
2021-12-30 10:00:00+00:00    47.0922
2021-12-30 11:00:00+00:00    46.4873
2021-12-30 12:00:00+00:00    46.6196
2021-12-30 13:00:00+00:00    46.6574
2021-12-30 14:00:00+00:00    46.3550
2021-12-30 15:00:00+00:00    46.1281
2021-12-31 06:00:00+00:00    46.2226
2021-12-31 07:00:00+00:00    46.0335
2021-12-31 08:00:00+00:00    46.3927
2021-12-31 09:00:00+00:00    46.4494
2021-12-31 10:00:00+00:00    46.0335
2021-12-31 11:00:00+00:00    46.2793
2021-12-31 12:00:00+00:00    46.1848
2021-12-31 13:00:00+00:00    46.2226
2021-12-31 14:00:00+00:00    45.9391
2021-12-31 15:00:00+00:00    45.7499
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    128.399994
2024-01-10 10:00:00+00:00    128.300000
2024-01-10 10:30:00+00:00    128.399994
2024-01-10 11:00:00+00:00    128.600000
2024-01-10 11:30:00+00:00    128.600006
2024-01-10 12:00:00+00:00    128.000000
2024-01-10 12:30:00+00:00    128.800003
2024-01-10 13:00:00+00:00    129.900000
2024-01-10 13:30:00+00:00    129.600006
2024-01-10 14:00:00+00:00    130.600000
2024-01-10 15:00:00+00:00    130.100000
2024-01-11 06:00:00+00:00    130.900000
2024-01-11 06:30:00+00:00    131.100006
2024-01-11 07:00:00+00:00    130.500000
2024-01-11 07:30:00+00:00    130.000000
2024-01-11 08:00:00+00:00    129.600000
2024-01-11 08:30:00+00:00    130.399994
2024-01-11 09:00:00+00:00    130.300000
2024-01-11 09:30:00+00:00    130.199997
2024-01-11 10:00:00+00:00    130.000000
2024-01-11 10:30:00+00:00    129.899994
2024-01-11 11:00:00+00:00    130.200000
2024-01-11 11:30:00+00:00    130.000000
2024-01-11 12:00:00+00:00    129.500000
2024-01-11 12:30:00+00:00    130.699997
2024-01-11 13:00:00+00:00    129.600000
2024-01-11 13:30:00+00:00    129.699997
2024-01-11 14:00:00+00:00    129.800000
2024-01-11 14:30:00+00:00    129.500000
2024-01-11 15:00:00+00:00    129.500000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    0.085300
2018-01-02 11:00:00+03:00   -0.119500
2018-01-02 12:00:00+03:00   -0.017100
2018-01-02 13:00:00+03:00    0.000000
2018-01-02 14:00:00+03:00    0.102500
                               ...   
2024-01-11 13:00:00+00:00   -1.099997
2024-01-11 13:30:00+00:00    0.099997
2024-01-11 14:00:00+00:00    0.100003
2024-01-11 14:30:00+00:00   -0.300000
2024-01-11 15:00:00+00:00    0.000000
Name: price, Length: 19223, dtype: float64
ADF Statistic: -19.99276333206753
p-value: 0.0
Critical Values: {'1%': -3.4306910248977847, '5%': -2.8616907206633995, '10%': -2.5668502245526077}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19224
Model:                ARIMA(30, 1, 1)   Log Likelihood              -19909.038
Date:                Thu, 11 Jan 2024   AIC                          39882.076
Time:                        21:17:02   BIC                          40133.720
Sample:                             0   HQIC                         39964.565
                              - 19224                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.2642      0.005   -249.021      0.000      -1.274      -1.254
ar.L2         -0.2695      0.006    -48.446      0.000      -0.280      -0.259
ar.L3          0.0120      0.006      1.881      0.060      -0.001       0.025
ar.L4         -0.0218      0.006     -3.401      0.001      -0.034      -0.009
ar.L5         -0.0008      0.006     -0.120      0.905      -0.013       0.012
ar.L6          0.0011      0.006      0.164      0.870      -0.011       0.014
ar.L7          0.0006      0.006      0.096      0.924      -0.012       0.013
ar.L8          0.0085      0.007      1.258      0.208      -0.005       0.022
ar.L9          0.0075      0.007      1.100      0.271      -0.006       0.021
ar.L10         0.0101      0.007      1.489      0.136      -0.003       0.023
ar.L11        -0.0136      0.007     -1.972      0.049      -0.027   -8.31e-05
ar.L12        -0.0184      0.007     -2.696      0.007      -0.032      -0.005
ar.L13         0.0036      0.007      0.512      0.609      -0.010       0.018
ar.L14         0.0151      0.007      2.121      0.034       0.001       0.029
ar.L15         0.0386      0.007      5.831      0.000       0.026       0.052
ar.L16        -0.0370      0.007     -5.377      0.000      -0.051      -0.024
ar.L17        -0.0441      0.007     -6.759      0.000      -0.057      -0.031
ar.L18         0.0652      0.006     10.735      0.000       0.053       0.077
ar.L19         0.2315      0.005     43.741      0.000       0.221       0.242
ar.L20         0.0334      0.005      6.128      0.000       0.023       0.044
ar.L21        -0.1456      0.006    -22.893      0.000      -0.158      -0.133
ar.L22        -0.0372      0.007     -5.220      0.000      -0.051      -0.023
ar.L23        -0.0353      0.007     -5.092      0.000      -0.049      -0.022
ar.L24        -0.0170      0.007     -2.491      0.013      -0.030      -0.004
ar.L25        -0.0142      0.007     -2.070      0.038      -0.028      -0.001
ar.L26         0.0013      0.007      0.191      0.849      -0.012       0.015
ar.L27        -0.0192      0.007     -2.678      0.007      -0.033      -0.005
ar.L28        -0.0233      0.007     -3.310      0.001      -0.037      -0.009
ar.L29         0.0295      0.007      4.221      0.000       0.016       0.043
ar.L30         0.0730      0.005     14.572      0.000       0.063       0.083
ma.L1          0.8940      0.004    232.990      0.000       0.886       0.901
sigma2         0.4639      0.002    260.055      0.000       0.460       0.467
===================================================================================
Ljung-Box (L1) (Q):                   0.88   Jarque-Bera (JB):            197613.42
Prob(Q):                              0.35   Prob(JB):                         0.00
Heteroskedasticity (H):              83.65   Skew:                             0.90
Prob(H) (two-sided):                  0.00   Kurtosis:                        18.60
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224    129.639104
19225    129.565381
19226    129.328436
19227    129.401643
19228    129.315117
19229    129.606430
19230    129.437279
19231    129.512839
19232    129.415826
19233    129.598379
Name: predicted_mean, dtype: float64
       lower price  upper price
19224   128.304145   130.974062
19225   127.987774   131.142987
19226   127.401898   131.254974
19227   127.266747   131.536540
19228   126.936687   131.693548
19229   127.045673   132.167187
19230   126.675710   132.198847
19231   126.589479   132.436199
19232   126.313576   132.518077
19233   126.348872   132.847885
                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00  10.9361      ASELS
2021-12-27 10:00:00+03:00  11.2048      ASELS
2021-12-27 11:00:00+03:00  11.1252      ASELS
2021-12-27 12:00:00+03:00  11.0655      ASELS
2021-12-27 13:00:00+03:00  11.0754      ASELS
...                            ...        ...
2024-01-11 14:00:00+03:00  47.1000      ASELS
2024-01-11 15:00:00+03:00  46.8000      ASELS
2024-01-11 16:00:00+03:00  46.8000      ASELS
2024-01-11 17:00:00+03:00  46.7200      ASELS
2024-01-11 18:00:00+03:00  46.8800      ASELS

[5104 rows x 2 columns]

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    10.9361
2021-12-27 07:00:00+00:00    11.2048
2021-12-27 08:00:00+00:00    11.1252
2021-12-27 09:00:00+00:00    11.0655
2021-12-27 10:00:00+00:00    11.0754
2021-12-27 11:00:00+00:00    10.9660
2021-12-27 12:00:00+00:00    10.9063
2021-12-27 13:00:00+00:00    10.8565
2021-12-27 14:00:00+00:00    10.8466
2021-12-27 15:00:00+00:00    10.8565
2021-12-28 06:00:00+00:00    10.9560
2021-12-28 07:00:00+00:00    10.9759
2021-12-28 08:00:00+00:00    10.8266
2021-12-28 09:00:00+00:00    10.7570
2021-12-28 10:00:00+00:00    10.7172
2021-12-28 11:00:00+00:00    10.6376
2021-12-28 12:00:00+00:00    10.7172
2021-12-28 13:00:00+00:00    10.6177
2021-12-28 14:00:00+00:00    10.4983
2021-12-28 15:00:00+00:00    10.5281
2021-12-29 06:00:00+00:00    10.4485
2021-12-29 07:00:00+00:00    10.3590
2021-12-29 08:00:00+00:00    10.6077
2021-12-29 09:00:00+00:00    10.7072
2021-12-29 10:00:00+00:00    10.7072
2021-12-29 11:00:00+00:00    10.7371
2021-12-29 12:00:00+00:00    10.7570
2021-12-29 13:00:00+00:00    10.8366
2021-12-29 14:00:00+00:00    10.7371
2021-12-29 15:00:00+00:00    10.7669
2021-12-30 06:00:00+00:00    10.9162
2021-12-30 07:00:00+00:00    10.7371
2021-12-30 08:00:00+00:00    10.8067
2021-12-30 09:00:00+00:00    10.7470
2021-12-30 10:00:00+00:00    10.7371
2021-12-30 11:00:00+00:00    10.5679
2021-12-30 12:00:00+00:00    10.6077
2021-12-30 13:00:00+00:00    10.6276
2021-12-30 14:00:00+00:00    10.5281
2021-12-30 15:00:00+00:00    10.5082
2021-12-31 06:00:00+00:00    10.5878
2021-12-31 07:00:00+00:00    10.5182
2021-12-31 08:00:00+00:00    10.5381
2021-12-31 09:00:00+00:00    10.5878
2021-12-31 10:00:00+00:00    10.4684
2021-12-31 11:00:00+00:00    10.5082
2021-12-31 12:00:00+00:00    10.4485
2021-12-31 13:00:00+00:00    10.4286
2021-12-31 14:00:00+00:00    10.3689
2021-12-31 15:00:00+00:00    10.3888
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    47.259998
2024-01-10 10:00:00+00:00    47.340000
2024-01-10 10:30:00+00:00    47.400002
2024-01-10 11:00:00+00:00    47.560000
2024-01-10 11:30:00+00:00    47.580002
2024-01-10 12:00:00+00:00    47.380000
2024-01-10 12:30:00+00:00    47.740002
2024-01-10 13:00:00+00:00    47.860000
2024-01-10 13:30:00+00:00    47.560001
2024-01-10 14:00:00+00:00    47.620000
2024-01-10 15:00:00+00:00    47.480000
2024-01-11 06:00:00+00:00    47.760000
2024-01-11 06:30:00+00:00    47.580002
2024-01-11 07:00:00+00:00    47.300000
2024-01-11 07:30:00+00:00    47.340000
2024-01-11 08:00:00+00:00    47.220000
2024-01-11 08:30:00+00:00    47.279999
2024-01-11 09:00:00+00:00    47.280000
2024-01-11 09:30:00+00:00    47.160000
2024-01-11 10:00:00+00:00    47.200000
2024-01-11 10:30:00+00:00    47.160000
2024-01-11 11:00:00+00:00    47.100000
2024-01-11 11:30:00+00:00    47.040001
2024-01-11 12:00:00+00:00    46.800000
2024-01-11 12:30:00+00:00    47.160000
2024-01-11 13:00:00+00:00    46.800000
2024-01-11 13:30:00+00:00    46.820000
2024-01-11 14:00:00+00:00    46.720000
2024-01-11 14:30:00+00:00    46.880001
2024-01-11 15:00:00+00:00    46.880000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    0.014400
2018-01-02 11:00:00+03:00   -0.014400
2018-01-02 12:00:00+03:00    0.028900
2018-01-02 13:00:00+03:00    0.004800
2018-01-02 14:00:00+03:00   -0.009600
                               ...   
2024-01-11 13:00:00+00:00   -0.360000
2024-01-11 13:30:00+00:00    0.020000
2024-01-11 14:00:00+00:00   -0.100000
2024-01-11 14:30:00+00:00    0.160001
2024-01-11 15:00:00+00:00   -0.000001
Name: price, Length: 19113, dtype: float64
ADF Statistic: -20.989558933444666
p-value: 0.0
Critical Values: {'1%': -3.4306929924764797, '5%': -2.8616915902117444, '10%': -2.5668506873985146}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19114
Model:                ARIMA(30, 3, 1)   Log Likelihood                9714.185
Date:                Thu, 11 Jan 2024   AIC                         -19364.370
Time:                        21:20:41   BIC                         -19112.914
Sample:                             0   HQIC                        -19281.920
                              - 19114                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.9564      0.003   -276.375      0.000      -0.963      -0.950
ar.L2         -0.8940      0.005   -182.493      0.000      -0.904      -0.884
ar.L3         -0.9071      0.006   -140.067      0.000      -0.920      -0.894
ar.L4         -0.8447      0.008   -104.861      0.000      -0.860      -0.829
ar.L5         -0.8353      0.009    -91.433      0.000      -0.853      -0.817
ar.L6         -0.8062      0.010    -78.091      0.000      -0.826      -0.786
ar.L7         -0.8021      0.011    -72.948      0.000      -0.824      -0.781
ar.L8         -0.7566      0.011    -66.156      0.000      -0.779      -0.734
ar.L9         -0.7532      0.012    -63.689      0.000      -0.776      -0.730
ar.L10        -0.6867      0.012    -56.338      0.000      -0.711      -0.663
ar.L11        -0.6313      0.013    -49.595      0.000      -0.656      -0.606
ar.L12        -0.5957      0.012    -48.075      0.000      -0.620      -0.571
ar.L13        -0.5622      0.013    -43.692      0.000      -0.587      -0.537
ar.L14        -0.5370      0.013    -40.609      0.000      -0.563      -0.511
ar.L15        -0.5052      0.013    -38.212      0.000      -0.531      -0.479
ar.L16        -0.4749      0.013    -35.714      0.000      -0.501      -0.449
ar.L17        -0.4199      0.013    -32.168      0.000      -0.445      -0.394
ar.L18        -0.4147      0.013    -31.783      0.000      -0.440      -0.389
ar.L19        -0.3895      0.013    -30.490      0.000      -0.415      -0.364
ar.L20        -0.3879      0.013    -30.975      0.000      -0.412      -0.363
ar.L21        -0.3948      0.012    -31.959      0.000      -0.419      -0.371
ar.L22        -0.3527      0.012    -28.474      0.000      -0.377      -0.328
ar.L23        -0.2953      0.012    -24.200      0.000      -0.319      -0.271
ar.L24        -0.2314      0.012    -19.148      0.000      -0.255      -0.208
ar.L25        -0.1947      0.011    -17.461      0.000      -0.217      -0.173
ar.L26        -0.1317      0.010    -12.931      0.000      -0.152      -0.112
ar.L27        -0.1180      0.009    -12.735      0.000      -0.136      -0.100
ar.L28        -0.0689      0.008     -8.452      0.000      -0.085      -0.053
ar.L29        -0.0545      0.006     -8.427      0.000      -0.067      -0.042
ar.L30        -0.0154      0.004     -3.498      0.000      -0.024      -0.007
ma.L1         -0.9960      0.002   -625.674      0.000      -0.999      -0.993
sigma2         0.0208   6.05e-05    344.160      0.000       0.021       0.021
===================================================================================
Ljung-Box (L1) (Q):                   8.07   Jarque-Bera (JB):            975374.60
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):              22.98   Skew:                             1.25
Prob(H) (two-sided):                  0.00   Kurtosis:                        37.91
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19114    46.886176
19115    46.845436
19116    46.851228
19117    46.816103
19118    46.822895
19119    46.784912
19120    46.747803
19121    46.714902
19122    46.665398
19123    46.638604
Name: predicted_mean, dtype: float64
       lower price  upper price
19114    46.603404    47.168947
19115    46.435909    47.254963
19116    46.334199    47.368257
19117    46.210830    47.421376
19118    46.131348    47.514441
19119    46.014450    47.555374
19120    45.900809    47.594798
19121    45.796426    47.633378
19122    45.674477    47.656320
19123    45.578761    47.698448

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

                              price short_name
timestamp                                     
2021-12-27 09:00:00+03:00   62.9265      BIMAS
2021-12-27 10:00:00+03:00   63.4541      BIMAS
2021-12-27 11:00:00+03:00   62.8305      BIMAS
2021-12-27 12:00:00+03:00   62.5908      BIMAS
2021-12-27 13:00:00+03:00   62.4948      BIMAS
...                             ...        ...
2024-01-11 14:00:00+03:00  325.0000      BIMAS
2024-01-11 15:00:00+03:00  323.5000      BIMAS
2024-01-11 16:00:00+03:00  324.5000      BIMAS
2024-01-11 17:00:00+03:00  327.2500      BIMAS
2024-01-11 18:00:00+03:00  328.2500      BIMAS

[5104 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    62.9265
2021-12-27 07:00:00+00:00    63.4541
2021-12-27 08:00:00+00:00    62.8305
2021-12-27 09:00:00+00:00    62.5908
2021-12-27 10:00:00+00:00    62.4948
2021-12-27 11:00:00+00:00    61.7753
2021-12-27 12:00:00+00:00    61.7753
2021-12-27 13:00:00+00:00    61.7274
2021-12-27 14:00:00+00:00    61.4396
2021-12-27 15:00:00+00:00    61.2957
2021-12-28 06:00:00+00:00    61.8714
2021-12-28 07:00:00+00:00    61.8233
2021-12-28 08:00:00+00:00    61.3916
2021-12-28 09:00:00+00:00    61.0080
2021-12-28 10:00:00+00:00    61.1519
2021-12-28 11:00:00+00:00    60.7202
2021-12-28 12:00:00+00:00    61.2478
2021-12-28 13:00:00+00:00    60.6243
2021-12-28 14:00:00+00:00    60.0007
2021-12-28 15:00:00+00:00    60.0488
2021-12-29 06:00:00+00:00    60.0967
2021-12-29 07:00:00+00:00    60.5764
2021-12-29 08:00:00+00:00    61.3916
2021-12-29 09:00:00+00:00    61.4396
2021-12-29 10:00:00+00:00    61.4396
2021-12-29 11:00:00+00:00    61.3437
2021-12-29 12:00:00+00:00    61.3437
2021-12-29 13:00:00+00:00    61.2957
2021-12-29 14:00:00+00:00    61.0559
2021-12-29 15:00:00+00:00    61.1040
2021-12-30 06:00:00+00:00    61.5835
2021-12-30 07:00:00+00:00    60.6722
2021-12-30 08:00:00+00:00    60.5764
2021-12-30 09:00:00+00:00    60.4805
2021-12-30 10:00:00+00:00    60.2405
2021-12-30 11:00:00+00:00    59.4732
2021-12-30 12:00:00+00:00    59.5691
2021-12-30 13:00:00+00:00    59.7130
2021-12-30 14:00:00+00:00    59.2813
2021-12-30 15:00:00+00:00    59.1375
2021-12-31 06:00:00+00:00    59.4253
2021-12-31 07:00:00+00:00    59.4253
2021-12-31 08:00:00+00:00    59.4732
2021-12-31 09:00:00+00:00    59.6170
2021-12-31 10:00:00+00:00    59.5212
2021-12-31 11:00:00+00:00    59.4732
2021-12-31 12:00:00+00:00    59.4253
2021-12-31 13:00:00+00:00    59.4732
2021-12-31 14:00:00+00:00    59.0895
2021-12-31 15:00:00+00:00    58.8017
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    315.50
2024-01-10 10:00:00+00:00    314.00
2024-01-10 10:30:00+00:00    314.25
2024-01-10 11:00:00+00:00    314.75
2024-01-10 11:30:00+00:00    314.50
2024-01-10 12:00:00+00:00    314.50
2024-01-10 12:30:00+00:00    319.00
2024-01-10 13:00:00+00:00    320.50
2024-01-10 13:30:00+00:00    318.25
2024-01-10 14:00:00+00:00    318.75
2024-01-10 15:00:00+00:00    318.25
2024-01-11 06:00:00+00:00    320.75
2024-01-11 06:30:00+00:00    324.25
2024-01-11 07:00:00+00:00    323.75
2024-01-11 07:30:00+00:00    324.75
2024-01-11 08:00:00+00:00    324.00
2024-01-11 08:30:00+00:00    329.00
2024-01-11 09:00:00+00:00    327.00
2024-01-11 09:30:00+00:00    325.75
2024-01-11 10:00:00+00:00    325.00
2024-01-11 10:30:00+00:00    325.75
2024-01-11 11:00:00+00:00    325.00
2024-01-11 11:30:00+00:00    325.25
2024-01-11 12:00:00+00:00    323.50
2024-01-11 12:30:00+00:00    327.00
2024-01-11 13:00:00+00:00    324.50
2024-01-11 13:30:00+00:00    325.25
2024-01-11 14:00:00+00:00    327.25
2024-01-11 14:30:00+00:00    328.25
2024-01-11 15:00:00+00:00    328.25
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    0.3467
2018-01-02 11:00:00+03:00   -0.1226
2018-01-02 12:00:00+03:00   -0.1833
2018-01-02 13:00:00+03:00    0.1426
2018-01-02 14:00:00+03:00    0.1428
                              ...  
2024-01-11 13:00:00+00:00   -2.5000
2024-01-11 13:30:00+00:00    0.7500
2024-01-11 14:00:00+00:00    2.0000
2024-01-11 14:30:00+00:00    1.0000
2024-01-11 15:00:00+00:00    0.0000
Name: price, Length: 19222, dtype: float64
ADF Statistic: -20.907466583883348
p-value: 0.0
Critical Values: {'1%': -3.4306910426831823, '5%': -2.86169072852345, '10%': -2.5668502287363797}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19223
Model:                ARIMA(30, 1, 1)   Log Likelihood              -26542.801
Date:                Thu, 11 Jan 2024   AIC                          53149.602
Time:                        21:23:33   BIC                          53401.244
Sample:                             0   HQIC                         53232.090
                              - 19223                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.2134      0.006   -194.614      0.000      -1.226      -1.201
ar.L2         -0.2345      0.006    -42.536      0.000      -0.245      -0.224
ar.L3         -0.0232      0.007     -3.542      0.000      -0.036      -0.010
ar.L4         -0.0281      0.008     -3.686      0.000      -0.043      -0.013
ar.L5          0.0047      0.007      0.645      0.519      -0.010       0.019
ar.L6          0.0176      0.007      2.484      0.013       0.004       0.032
ar.L7          0.0352      0.007      5.012      0.000       0.021       0.049
ar.L8          0.0261      0.007      3.497      0.000       0.011       0.041
ar.L9          0.0329      0.008      4.304      0.000       0.018       0.048
ar.L10         0.0450      0.008      5.975      0.000       0.030       0.060
ar.L11         0.0473      0.008      6.135      0.000       0.032       0.062
ar.L12         0.0253      0.007      3.420      0.001       0.011       0.040
ar.L13         0.0263      0.007      3.821      0.000       0.013       0.040
ar.L14         0.0189      0.007      2.619      0.009       0.005       0.033
ar.L15         0.0278      0.007      3.771      0.000       0.013       0.042
ar.L16        -0.0193      0.008     -2.550      0.011      -0.034      -0.004
ar.L17        -0.0417      0.007     -5.774      0.000      -0.056      -0.028
ar.L18         0.0792      0.006     12.662      0.000       0.067       0.091
ar.L19         0.1835      0.005     39.260      0.000       0.174       0.193
ar.L20        -0.0059      0.005     -1.108      0.268      -0.016       0.005
ar.L21        -0.1056      0.006    -16.790      0.000      -0.118      -0.093
ar.L22        -0.0403      0.007     -5.718      0.000      -0.054      -0.027
ar.L23        -0.0596      0.008     -7.758      0.000      -0.075      -0.045
ar.L24        -0.0326      0.008     -4.124      0.000      -0.048      -0.017
ar.L25        -0.0146      0.008     -1.829      0.067      -0.030       0.001
ar.L26         0.0015      0.008      0.181      0.856      -0.015       0.018
ar.L27        -0.0176      0.008     -2.246      0.025      -0.033      -0.002
ar.L28        -0.0392      0.008     -5.018      0.000      -0.054      -0.024
ar.L29        -0.0059      0.008     -0.730      0.465      -0.022       0.010
ar.L30         0.0600      0.006     10.263      0.000       0.049       0.071
ma.L1          0.8783      0.005    172.835      0.000       0.868       0.888
sigma2         0.9266      0.003    299.084      0.000       0.921       0.933
===================================================================================
Ljung-Box (L1) (Q):                   0.12   Jarque-Bera (JB):            584358.02
Prob(Q):                              0.73   Prob(JB):                         0.00
Heteroskedasticity (H):              30.42   Skew:                             0.28
Prob(H) (two-sided):                  0.00   Kurtosis:                        30.01
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223    328.780623
19224    328.660012
19225    328.225624
19226    328.257666
19227    328.299201
19228    328.808080
19229    328.013440
19230    328.242287
19231    328.077706
19232    328.298680
Name: predicted_mean, dtype: float64
       lower price  upper price
19223   326.893950   330.667296
19224   326.394340   330.925683
19225   325.463965   330.987282
19226   325.209720   331.305612
19227   324.890420   331.707983
19228   325.150414   332.465746
19229   324.044233   331.982646
19230   324.037818   332.446756
19231   323.595485   332.559927
19232   323.590514   333.006846
                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00   2.6366      DOHOL
2021-12-27 10:00:00+03:00   2.6736      DOHOL
2021-12-27 11:00:00+03:00   2.6366      DOHOL
2021-12-27 12:00:00+03:00   2.6458      DOHOL
2021-12-27 13:00:00+03:00   2.6458      DOHOL
...                            ...        ...
2024-01-11 14:00:00+03:00  12.0200      DOHOL
2024-01-11 15:00:00+03:00  11.9200      DOHOL
2024-01-11 16:00:00+03:00  12.0000      DOHOL
2024-01-11 17:00:00+03:00  11.9900      DOHOL
2024-01-11 18:00:00+03:00  11.9900      DOHOL

[5104 rows x 2 columns]

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    2.6366
2021-12-27 07:00:00+00:00    2.6736
2021-12-27 08:00:00+00:00    2.6366
2021-12-27 09:00:00+00:00    2.6458
2021-12-27 10:00:00+00:00    2.6458
2021-12-27 11:00:00+00:00    2.6180
2021-12-27 12:00:00+00:00    2.6086
2021-12-27 13:00:00+00:00    2.6086
2021-12-27 14:00:00+00:00    2.5901
2021-12-27 15:00:00+00:00    2.5809
2021-12-28 06:00:00+00:00    2.6086
2021-12-28 07:00:00+00:00    2.6366
2021-12-28 08:00:00+00:00    2.6086
2021-12-28 09:00:00+00:00    2.5901
2021-12-28 10:00:00+00:00    2.6086
2021-12-28 11:00:00+00:00    2.5901
2021-12-28 12:00:00+00:00    2.5901
2021-12-28 13:00:00+00:00    2.5715
2021-12-28 14:00:00+00:00    2.5529
2021-12-28 15:00:00+00:00    2.5437
2021-12-29 06:00:00+00:00    2.5622
2021-12-29 07:00:00+00:00    2.5529
2021-12-29 08:00:00+00:00    2.5809
2021-12-29 09:00:00+00:00    2.5901
2021-12-29 10:00:00+00:00    2.5901
2021-12-29 11:00:00+00:00    2.5901
2021-12-29 12:00:00+00:00    2.5901
2021-12-29 13:00:00+00:00    2.6272
2021-12-29 14:00:00+00:00    2.6180
2021-12-29 15:00:00+00:00    2.6180
2021-12-30 06:00:00+00:00    2.6550
2021-12-30 07:00:00+00:00    2.6366
2021-12-30 08:00:00+00:00    2.6180
2021-12-30 09:00:00+00:00    2.6086
2021-12-30 10:00:00+00:00    2.6180
2021-12-30 11:00:00+00:00    2.5809
2021-12-30 12:00:00+00:00    2.5901
2021-12-30 13:00:00+00:00    2.5993
2021-12-30 14:00:00+00:00    2.5993
2021-12-30 15:00:00+00:00    2.5809
2021-12-31 06:00:00+00:00    2.5809
2021-12-31 07:00:00+00:00    2.5809
2021-12-31 08:00:00+00:00    2.5993
2021-12-31 09:00:00+00:00    2.5901
2021-12-31 10:00:00+00:00    2.5901
2021-12-31 11:00:00+00:00    2.5901
2021-12-31 12:00:00+00:00    2.5809
2021-12-31 13:00:00+00:00    2.5809
2021-12-31 14:00:00+00:00    2.5437
2021-12-31 15:00:00+00:00    2.5529
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    11.96
2024-01-10 10:00:00+00:00    11.97
2024-01-10 10:30:00+00:00    11.99
2024-01-10 11:00:00+00:00    12.03
2024-01-10 11:30:00+00:00    12.08
2024-01-10 12:00:00+00:00    11.98
2024-01-10 12:30:00+00:00    12.07
2024-01-10 13:00:00+00:00    12.06
2024-01-10 13:30:00+00:00    12.08
2024-01-10 14:00:00+00:00    12.11
2024-01-10 15:00:00+00:00    12.11
2024-01-11 06:00:00+00:00    12.15
2024-01-11 06:30:00+00:00    12.15
2024-01-11 07:00:00+00:00    12.06
2024-01-11 07:30:00+00:00    12.04
2024-01-11 08:00:00+00:00    12.01
2024-01-11 08:30:00+00:00    12.07
2024-01-11 09:00:00+00:00    12.08
2024-01-11 09:30:00+00:00    12.07
2024-01-11 10:00:00+00:00    12.03
2024-01-11 10:30:00+00:00    12.00
2024-01-11 11:00:00+00:00    12.02
2024-01-11 11:30:00+00:00    12.02
2024-01-11 12:00:00+00:00    11.92
2024-01-11 12:30:00+00:00    12.04
2024-01-11 13:00:00+00:00    12.00
2024-01-11 13:30:00+00:00    11.99
2024-01-11 14:00:00+00:00    11.99
2024-01-11 14:30:00+00:00    11.99
2024-01-11 15:00:00+00:00    11.99
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    8.200000e-03
2018-01-02 11:00:00+03:00    0.000000e+00
2018-01-02 12:00:00+03:00    0.000000e+00
2018-01-02 13:00:00+03:00    7.900000e-03
2018-01-02 14:00:00+03:00   -7.900000e-03
                                 ...     
2024-01-11 13:00:00+00:00   -3.999996e-02
2024-01-11 13:30:00+00:00   -1.000023e-02
2024-01-11 14:00:00+00:00    2.288818e-07
2024-01-11 14:30:00+00:00   -2.288818e-07
2024-01-11 15:00:00+00:00    2.288818e-07
Name: price, Length: 19222, dtype: float64
ADF Statistic: -22.845154701723008
p-value: 0.0
Critical Values: {'1%': -3.4306910248977847, '5%': -2.8616907206633995, '10%': -2.5668502245526077}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19223
Model:                 ARIMA(2, 1, 1)   Log Likelihood               23303.212
Date:                Thu, 11 Jan 2024   AIC                         -46598.424
Time:                        21:23:42   BIC                         -46566.969
Sample:                             0   HQIC                        -46588.113
                              - 19223                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1872      0.011   -103.925      0.000      -1.210      -1.165
ar.L2         -0.2229      0.010    -21.260      0.000      -0.243      -0.202
ma.L1          0.6019      0.010     60.047      0.000       0.582       0.622
sigma2         0.0052   2.11e-05    245.445      0.000       0.005       0.005
===================================================================================
Ljung-Box (L1) (Q):                   1.62   Jarque-Bera (JB):            190640.66
Prob(Q):                              0.20   Prob(JB):                         0.00
Heteroskedasticity (H):              80.60   Skew:                             0.73
Prob(H) (two-sided):                  0.00   Kurtosis:                        18.36
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223    11.993648
19224    11.989317
19225    11.993646
19226    11.989472
19227    11.993463
19228    11.989655
19229    11.993286
19230    11.989823
19231    11.993125
19232    11.989977
Name: predicted_mean, dtype: float64
       lower price  upper price
19223    11.852567    12.134729
19224    11.836585    12.142049
19225    11.796220    12.191072
19226    11.781794    12.197149
19227    11.752796    12.234130
19228    11.739767    12.239542
19229    11.716488    12.270085
19230    11.704391    12.275255
19231    11.684737    12.301514
19232    11.673326    12.306627
                            price short_name
timestamp                                   
2021-12-27 09:00:00+03:00  2.0714      EKGYO
2021-12-27 10:00:00+03:00  2.0993      EKGYO
2021-12-27 11:00:00+03:00  2.0993      EKGYO
2021-12-27 12:00:00+03:00  2.0901      EKGYO
2021-12-27 13:00:00+03:00  2.0901      EKGYO
...                           ...        ...
2024-01-11 14:00:00+03:00  8.2100      EKGYO
2024-01-11 15:00:00+03:00  8.1500      EKGYO
2024-01-11 16:00:00+03:00  8.1300      EKGYO
2024-01-11 17:00:00+03:00  8.1700      EKGYO
2024-01-11 18:00:00+03:00  8.1700      EKGYO

[5104 rows x 2 columns]

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    2.0714
2021-12-27 07:00:00+00:00    2.0993
2021-12-27 08:00:00+00:00    2.0993
2021-12-27 09:00:00+00:00    2.0901
2021-12-27 10:00:00+00:00    2.0901
2021-12-27 11:00:00+00:00    2.0620
2021-12-27 12:00:00+00:00    2.0807
2021-12-27 13:00:00+00:00    2.0901
2021-12-27 14:00:00+00:00    2.0714
2021-12-27 15:00:00+00:00    2.0714
2021-12-28 06:00:00+00:00    2.1086
2021-12-28 07:00:00+00:00    2.1460
2021-12-28 08:00:00+00:00    2.1273
2021-12-28 09:00:00+00:00    2.0901
2021-12-28 10:00:00+00:00    2.0901
2021-12-28 11:00:00+00:00    2.0620
2021-12-28 12:00:00+00:00    2.0807
2021-12-28 13:00:00+00:00    2.0714
2021-12-28 14:00:00+00:00    2.0527
2021-12-28 15:00:00+00:00    2.0527
2021-12-29 06:00:00+00:00    2.0620
2021-12-29 07:00:00+00:00    2.0247
2021-12-29 08:00:00+00:00    2.0247
2021-12-29 09:00:00+00:00    2.0247
2021-12-29 10:00:00+00:00    2.0341
2021-12-29 11:00:00+00:00    2.0341
2021-12-29 12:00:00+00:00    2.0620
2021-12-29 13:00:00+00:00    2.0620
2021-12-29 14:00:00+00:00    2.0433
2021-12-29 15:00:00+00:00    2.0527
2021-12-30 06:00:00+00:00    2.0714
2021-12-30 07:00:00+00:00    2.0527
2021-12-30 08:00:00+00:00    2.0527
2021-12-30 09:00:00+00:00    2.0433
2021-12-30 10:00:00+00:00    2.0620
2021-12-30 11:00:00+00:00    2.0341
2021-12-30 12:00:00+00:00    2.0620
2021-12-30 13:00:00+00:00    2.0620
2021-12-30 14:00:00+00:00    2.0433
2021-12-30 15:00:00+00:00    2.0433
2021-12-31 06:00:00+00:00    2.0527
2021-12-31 07:00:00+00:00    2.0433
2021-12-31 08:00:00+00:00    2.0527
2021-12-31 09:00:00+00:00    2.0433
2021-12-31 10:00:00+00:00    2.0433
2021-12-31 11:00:00+00:00    2.0341
2021-12-31 12:00:00+00:00    2.0433
2021-12-31 13:00:00+00:00    2.0247
2021-12-31 14:00:00+00:00    1.9967
2021-12-31 15:00:00+00:00    1.9967
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    8.07
2024-01-10 10:00:00+00:00    8.12
2024-01-10 10:30:00+00:00    8.14
2024-01-10 11:00:00+00:00    8.13
2024-01-10 11:30:00+00:00    8.13
2024-01-10 12:00:00+00:00    8.11
2024-01-10 12:30:00+00:00    8.15
2024-01-10 13:00:00+00:00    8.15
2024-01-10 13:30:00+00:00    8.23
2024-01-10 14:00:00+00:00    8.22
2024-01-10 15:00:00+00:00    8.25
2024-01-11 06:00:00+00:00    8.25
2024-01-11 06:30:00+00:00    8.27
2024-01-11 07:00:00+00:00    8.22
2024-01-11 07:30:00+00:00    8.20
2024-01-11 08:00:00+00:00    8.20
2024-01-11 08:30:00+00:00    8.20
2024-01-11 09:00:00+00:00    8.21
2024-01-11 09:30:00+00:00    8.22
2024-01-11 10:00:00+00:00    8.21
2024-01-11 10:30:00+00:00    8.22
2024-01-11 11:00:00+00:00    8.21
2024-01-11 11:30:00+00:00    8.22
2024-01-11 12:00:00+00:00    8.15
2024-01-11 12:30:00+00:00    8.22
2024-01-11 13:00:00+00:00    8.13
2024-01-11 13:30:00+00:00    8.14
2024-01-11 14:00:00+00:00    8.17
2024-01-11 14:30:00+00:00    8.17
2024-01-11 15:00:00+00:00    8.17
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    2.460000e-02
2018-01-02 11:00:00+03:00   -8.100000e-03
2018-01-02 12:00:00+03:00    8.100000e-03
2018-01-02 13:00:00+03:00    0.000000e+00
2018-01-02 14:00:00+03:00    8.100000e-03
                                 ...     
2024-01-11 13:00:00+00:00   -9.000027e-02
2024-01-11 13:30:00+00:00    1.000034e-02
2024-01-11 14:00:00+00:00    2.999966e-02
2024-01-11 14:30:00+00:00    7.629395e-08
2024-01-11 15:00:00+00:00   -7.629395e-08
Name: price, Length: 19223, dtype: float64
ADF Statistic: -20.102925907656232
p-value: 0.0
Critical Values: {'1%': -3.4306910248977847, '5%': -2.8616907206633995, '10%': -2.5668502245526077}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19224
Model:                 ARIMA(2, 1, 1)   Log Likelihood               29396.153
Date:                Thu, 11 Jan 2024   AIC                         -58784.307
Time:                        21:23:59   BIC                         -58752.851
Sample:                             0   HQIC                        -58773.996
                              - 19224                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.2271      0.007   -188.312      0.000      -1.240      -1.214
ar.L2         -0.2469      0.006    -40.505      0.000      -0.259      -0.235
ma.L1          0.7118      0.005    132.477      0.000       0.701       0.722
sigma2         0.0027   9.47e-06    290.256      0.000       0.003       0.003
===================================================================================
Ljung-Box (L1) (Q):                   0.95   Jarque-Bera (JB):            299599.18
Prob(Q):                              0.33   Prob(JB):                         0.00
Heteroskedasticity (H):              39.00   Skew:                             0.91
Prob(H) (two-sided):                  0.00   Kurtosis:                        22.26
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224    8.176089
19225    8.168617
19226    8.176283
19227    8.168721
19228    8.176107
19229    8.168911
19230    8.175917
19231    8.169097
19232    8.175736
19233    8.169273
Name: predicted_mean, dtype: float64
       lower price  upper price
19224     8.073310     8.278869
19225     8.054400     8.282834
19226     8.031222     8.321343
19227     8.013970     8.323473
19228     7.998226     8.353988
19229     7.982723     8.355100
19230     7.970492     8.381343
19231     7.956214     8.381980
19232     7.946150     8.405322
19233     7.932800     8.405746
                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00  25.0042      EREGL
2021-12-27 10:00:00+03:00  25.6008      EREGL
2021-12-27 11:00:00+03:00  25.4955      EREGL
2021-12-27 12:00:00+03:00  25.3902      EREGL
2021-12-27 13:00:00+03:00  25.5130      EREGL
...                            ...        ...
2024-01-11 14:00:00+03:00  44.2400      EREGL
2024-01-11 15:00:00+03:00  44.0600      EREGL
2024-01-11 16:00:00+03:00  43.8600      EREGL
2024-01-11 17:00:00+03:00  43.8600      EREGL
2024-01-11 18:00:00+03:00  43.9000      EREGL

[5104 rows x 2 columns]

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    25.0042
2021-12-27 07:00:00+00:00    25.6008
2021-12-27 08:00:00+00:00    25.4955
2021-12-27 09:00:00+00:00    25.3902
2021-12-27 10:00:00+00:00    25.5130
2021-12-27 11:00:00+00:00    25.3376
2021-12-27 12:00:00+00:00    25.3726
2021-12-27 13:00:00+00:00    25.4077
2021-12-27 14:00:00+00:00    25.1972
2021-12-27 15:00:00+00:00    25.1972
2021-12-28 06:00:00+00:00    25.5130
2021-12-28 07:00:00+00:00    25.4253
2021-12-28 08:00:00+00:00    25.2849
2021-12-28 09:00:00+00:00    25.2674
2021-12-28 10:00:00+00:00    25.3200
2021-12-28 11:00:00+00:00    25.0217
2021-12-28 12:00:00+00:00    25.0568
2021-12-28 13:00:00+00:00    25.0393
2021-12-28 14:00:00+00:00    24.4602
2021-12-28 15:00:00+00:00    24.4076
2021-12-29 06:00:00+00:00    24.4076
2021-12-29 07:00:00+00:00    24.9515
2021-12-29 08:00:00+00:00    25.2498
2021-12-29 09:00:00+00:00    25.2323
2021-12-29 10:00:00+00:00    25.4779
2021-12-29 11:00:00+00:00    25.2674
2021-12-29 12:00:00+00:00    25.4779
2021-12-29 13:00:00+00:00    25.4428
2021-12-29 14:00:00+00:00    25.6885
2021-12-29 15:00:00+00:00    25.6885
2021-12-30 06:00:00+00:00    26.2324
2021-12-30 07:00:00+00:00    25.6183
2021-12-30 08:00:00+00:00    25.6885
2021-12-30 09:00:00+00:00    25.5657
2021-12-30 10:00:00+00:00    25.5130
2021-12-30 11:00:00+00:00    24.9515
2021-12-30 12:00:00+00:00    25.0744
2021-12-30 13:00:00+00:00    25.0744
2021-12-30 14:00:00+00:00    24.9866
2021-12-30 15:00:00+00:00    24.8813
2021-12-31 06:00:00+00:00    24.8989
2021-12-31 07:00:00+00:00    25.0217
2021-12-31 08:00:00+00:00    24.9866
2021-12-31 09:00:00+00:00    25.0042
2021-12-31 10:00:00+00:00    24.6883
2021-12-31 11:00:00+00:00    24.7410
2021-12-31 12:00:00+00:00    24.7234
2021-12-31 13:00:00+00:00    25.0042
2021-12-31 14:00:00+00:00    24.6708
2021-12-31 15:00:00+00:00    24.7059
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    45.119999
2024-01-10 10:00:00+00:00    44.760000
2024-01-10 10:30:00+00:00    44.980000
2024-01-10 11:00:00+00:00    44.860000
2024-01-10 11:30:00+00:00    44.799999
2024-01-10 12:00:00+00:00    44.780000
2024-01-10 12:30:00+00:00    44.900002
2024-01-10 13:00:00+00:00    44.980000
2024-01-10 13:30:00+00:00    44.900002
2024-01-10 14:00:00+00:00    45.100000
2024-01-10 15:00:00+00:00    45.040000
2024-01-11 06:00:00+00:00    44.880000
2024-01-11 06:30:00+00:00    44.880001
2024-01-11 07:00:00+00:00    44.480000
2024-01-11 07:30:00+00:00    44.060001
2024-01-11 08:00:00+00:00    44.300000
2024-01-11 08:30:00+00:00    44.259998
2024-01-11 09:00:00+00:00    44.340000
2024-01-11 09:30:00+00:00    44.240002
2024-01-11 10:00:00+00:00    44.220000
2024-01-11 10:30:00+00:00    44.240002
2024-01-11 11:00:00+00:00    44.240000
2024-01-11 11:30:00+00:00    44.220001
2024-01-11 12:00:00+00:00    44.060000
2024-01-11 12:30:00+00:00    44.380001
2024-01-11 13:00:00+00:00    43.860000
2024-01-11 13:30:00+00:00    43.980000
2024-01-11 14:00:00+00:00    43.860000
2024-01-11 14:30:00+00:00    43.900002
2024-01-11 15:00:00+00:00    43.900000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    0.017800
2018-01-02 11:00:00+03:00   -0.023700
2018-01-02 12:00:00+03:00    0.000000
2018-01-02 13:00:00+03:00    0.011800
2018-01-02 14:00:00+03:00    0.023700
                               ...   
2024-01-11 13:00:00+00:00   -0.520001
2024-01-11 13:30:00+00:00    0.120000
2024-01-11 14:00:00+00:00   -0.120000
2024-01-11 14:30:00+00:00    0.040002
2024-01-11 15:00:00+00:00   -0.000002
Name: price, Length: 19222, dtype: float64
ADF Statistic: -22.17100995605609
p-value: 0.0
Critical Values: {'1%': -3.4306910604704344, '5%': -2.86169073638432, '10%': -2.566850232920588}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19223
Model:                ARIMA(30, 3, 1)   Log Likelihood                -916.606
Date:                Thu, 11 Jan 2024   AIC                           1897.212
Time:                        21:26:54   BIC                           2148.850
Sample:                             0   HQIC                          1979.699
                              - 19223                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.3563      0.003   -437.973      0.000      -1.362      -1.350
ar.L2         -1.2180      0.007   -180.717      0.000      -1.231      -1.205
ar.L3         -1.3049      0.010   -132.570      0.000      -1.324      -1.286
ar.L4         -1.2078      0.013    -93.329      0.000      -1.233      -1.182
ar.L5         -1.2639      0.015    -83.103      0.000      -1.294      -1.234
ar.L6         -1.1693      0.017    -67.339      0.000      -1.203      -1.135
ar.L7         -1.1623      0.019    -59.887      0.000      -1.200      -1.124
ar.L8         -1.0995      0.021    -52.261      0.000      -1.141      -1.058
ar.L9         -1.0244      0.022    -46.282      0.000      -1.068      -0.981
ar.L10        -0.9585      0.023    -41.756      0.000      -1.003      -0.913
ar.L11        -0.9117      0.024    -38.046      0.000      -0.959      -0.865
ar.L12        -0.8869      0.025    -36.030      0.000      -0.935      -0.839
ar.L13        -0.7952      0.025    -31.542      0.000      -0.845      -0.746
ar.L14        -0.7561      0.025    -29.786      0.000      -0.806      -0.706
ar.L15        -0.7148      0.025    -28.304      0.000      -0.764      -0.665
ar.L16        -0.7651      0.025    -30.482      0.000      -0.814      -0.716
ar.L17        -0.8891      0.025    -35.681      0.000      -0.938      -0.840
ar.L18        -0.3093      0.025    -12.377      0.000      -0.358      -0.260
ar.L19        -0.2443      0.024    -10.223      0.000      -0.291      -0.197
ar.L20        -0.4299      0.023    -18.895      0.000      -0.475      -0.385
ar.L21        -0.3978      0.022    -18.139      0.000      -0.441      -0.355
ar.L22        -0.4476      0.021    -21.520      0.000      -0.488      -0.407
ar.L23        -0.3591      0.020    -18.021      0.000      -0.398      -0.320
ar.L24        -0.3559      0.019    -19.210      0.000      -0.392      -0.320
ar.L25        -0.2763      0.017    -16.219      0.000      -0.310      -0.243
ar.L26        -0.2215      0.016    -14.240      0.000      -0.252      -0.191
ar.L27        -0.1660      0.014    -11.811      0.000      -0.194      -0.138
ar.L28        -0.1113      0.012     -9.658      0.000      -0.134      -0.089
ar.L29        -0.0640      0.009     -7.123      0.000      -0.082      -0.046
ar.L30        -0.0026      0.005     -0.522      0.602      -0.012       0.007
ma.L1         -0.9626      0.002   -401.959      0.000      -0.967      -0.958
sigma2         0.0656      0.000    405.043      0.000       0.065       0.066
===================================================================================
Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):           2061947.21
Prob(Q):                              0.93   Prob(JB):                         0.00
Heteroskedasticity (H):              25.40   Skew:                             0.38
Prob(H) (two-sided):                  0.00   Kurtosis:                        53.74
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223    44.033978
19224    43.658988
19225    43.521927
19226    43.609785
19227    43.599641
19228    43.543962
19229    43.450074
19230    43.335761
19231    43.282727
19232    43.190636
Name: predicted_mean, dtype: float64
       lower price  upper price
19223    43.531840    44.536116
19224    43.051440    44.266535
19225    42.743100    44.300755
19226    42.739416    44.480154
19227    42.583037    44.616245
19228    42.441955    44.645969
19229    42.205755    44.694393
19230    41.999444    44.672078
19231    41.804908    44.760546
19232    41.604964    44.776308

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

                              price short_name
timestamp                                     
2021-12-27 09:00:00+03:00  216.8891      FROTO
2021-12-27 10:00:00+03:00  225.8110      FROTO
2021-12-27 11:00:00+03:00  223.8482      FROTO
2021-12-27 12:00:00+03:00  221.4393      FROTO
2021-12-27 13:00:00+03:00  221.6177      FROTO
...                             ...        ...
2024-01-11 14:00:00+03:00  776.5000      FROTO
2024-01-11 15:00:00+03:00  777.0000      FROTO
2024-01-11 16:00:00+03:00  783.0000      FROTO
2024-01-11 17:00:00+03:00  780.5000      FROTO
2024-01-11 18:00:00+03:00  778.0000      FROTO

[5104 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    216.8891
2021-12-27 07:00:00+00:00    225.8110
2021-12-27 08:00:00+00:00    223.8482
2021-12-27 09:00:00+00:00    221.4393
2021-12-27 10:00:00+00:00    221.6177
2021-12-27 11:00:00+00:00    217.9597
2021-12-27 12:00:00+00:00    218.5844
2021-12-27 13:00:00+00:00    218.6735
2021-12-27 14:00:00+00:00    218.4952
2021-12-27 15:00:00+00:00    217.7814
2021-12-28 06:00:00+00:00    219.9225
2021-12-28 07:00:00+00:00    219.5657
2021-12-28 08:00:00+00:00    216.8891
2021-12-28 09:00:00+00:00    216.5323
2021-12-28 10:00:00+00:00    217.5137
2021-12-28 11:00:00+00:00    214.3018
2021-12-28 12:00:00+00:00    216.6215
2021-12-28 13:00:00+00:00    214.4803
2021-12-28 14:00:00+00:00    208.9487
2021-12-28 15:00:00+00:00    212.4282
2021-12-29 06:00:00+00:00    215.8186
2021-12-29 07:00:00+00:00    213.5881
2021-12-29 08:00:00+00:00    216.1754
2021-12-29 09:00:00+00:00    217.6029
2021-12-29 10:00:00+00:00    216.4431
2021-12-29 11:00:00+00:00    216.2646
2021-12-29 12:00:00+00:00    217.9597
2021-12-29 13:00:00+00:00    218.0490
2021-12-29 14:00:00+00:00    217.7814
2021-12-29 15:00:00+00:00    216.7107
2021-12-30 06:00:00+00:00    218.1381
2021-12-30 07:00:00+00:00    216.2646
2021-12-30 08:00:00+00:00    217.7814
2021-12-30 09:00:00+00:00    216.6215
2021-12-30 10:00:00+00:00    215.9969
2021-12-30 11:00:00+00:00    212.6958
2021-12-30 12:00:00+00:00    214.3910
2021-12-30 13:00:00+00:00    213.7665
2021-12-30 14:00:00+00:00    214.9264
2021-12-30 15:00:00+00:00    213.5881
2021-12-31 06:00:00+00:00    214.1234
2021-12-31 07:00:00+00:00    214.3018
2021-12-31 08:00:00+00:00    216.1754
2021-12-31 09:00:00+00:00    216.1754
2021-12-31 10:00:00+00:00    215.5508
2021-12-31 11:00:00+00:00    216.3538
2021-12-31 12:00:00+00:00    213.7665
2021-12-31 13:00:00+00:00    215.2832
2021-12-31 14:00:00+00:00    211.6252
2021-12-31 15:00:00+00:00    212.4282
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    753.5
2024-01-10 10:00:00+00:00    751.5
2024-01-10 10:30:00+00:00    752.5
2024-01-10 11:00:00+00:00    754.0
2024-01-10 11:30:00+00:00    760.0
2024-01-10 12:00:00+00:00    762.0
2024-01-10 12:30:00+00:00    763.0
2024-01-10 13:00:00+00:00    771.5
2024-01-10 13:30:00+00:00    766.5
2024-01-10 14:00:00+00:00    768.5
2024-01-10 15:00:00+00:00    768.0
2024-01-11 06:00:00+00:00    772.0
2024-01-11 06:30:00+00:00    773.0
2024-01-11 07:00:00+00:00    768.5
2024-01-11 07:30:00+00:00    770.0
2024-01-11 08:00:00+00:00    769.5
2024-01-11 08:30:00+00:00    772.5
2024-01-11 09:00:00+00:00    774.0
2024-01-11 09:30:00+00:00    775.5
2024-01-11 10:00:00+00:00    776.5
2024-01-11 10:30:00+00:00    776.0
2024-01-11 11:00:00+00:00    776.5
2024-01-11 11:30:00+00:00    777.0
2024-01-11 12:00:00+00:00    777.0
2024-01-11 12:30:00+00:00    785.0
2024-01-11 13:00:00+00:00    783.0
2024-01-11 13:30:00+00:00    780.0
2024-01-11 14:00:00+00:00    780.5
2024-01-11 14:30:00+00:00    778.0
2024-01-11 15:00:00+00:00    778.0
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    0.2094
2018-01-02 11:00:00+03:00    0.3142
2018-01-02 12:00:00+03:00    0.1744
2018-01-02 13:00:00+03:00   -0.1395
2018-01-02 14:00:00+03:00    0.2791
                              ...  
2024-01-11 13:00:00+00:00   -2.0000
2024-01-11 13:30:00+00:00   -3.0000
2024-01-11 14:00:00+00:00    0.5000
2024-01-11 14:30:00+00:00   -2.5000
2024-01-11 15:00:00+00:00    0.0000
Name: price, Length: 19221, dtype: float64
ADF Statistic: -22.69384925598741
p-value: 0.0
Critical Values: {'1%': -3.4306910604704344, '5%': -2.86169073638432, '10%': -2.566850232920588}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19222
Model:                ARIMA(30, 1, 1)   Log Likelihood              -58631.679
Date:                Thu, 11 Jan 2024   AIC                         117327.359
Time:                        21:29:38   BIC                         117578.999
Sample:                             0   HQIC                        117409.846
                              - 19222                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.7644      0.173     -4.417      0.000      -1.104      -0.425
ar.L2         -0.0801      0.151     -0.530      0.596      -0.377       0.216
ar.L3         -0.0528      0.030     -1.736      0.083      -0.112       0.007
ar.L4          0.0041      0.014      0.300      0.764      -0.023       0.031
ar.L5         -0.0689      0.006    -11.273      0.000      -0.081      -0.057
ar.L6          0.0059      0.014      0.432      0.666      -0.021       0.033
ar.L7         -0.0272      0.007     -4.017      0.000      -0.040      -0.014
ar.L8          0.0286      0.009      3.316      0.001       0.012       0.045
ar.L9         -0.0338      0.008     -4.203      0.000      -0.050      -0.018
ar.L10         0.0431      0.010      4.465      0.000       0.024       0.062
ar.L11         0.0028      0.010      0.295      0.768      -0.016       0.022
ar.L12         0.0165      0.007      2.263      0.024       0.002       0.031
ar.L13         0.0028      0.008      0.378      0.705      -0.012       0.018
ar.L14        -0.0119      0.007     -1.736      0.083      -0.025       0.002
ar.L15        -0.0212      0.007     -3.226      0.001      -0.034      -0.008
ar.L16        -0.1635      0.006    -25.227      0.000      -0.176      -0.151
ar.L17        -0.0532      0.029     -1.833      0.067      -0.110       0.004
ar.L18         0.1861      0.013     14.403      0.000       0.161       0.211
ar.L19         0.4670      0.031     15.008      0.000       0.406       0.528
ar.L20        -0.0341      0.084     -0.403      0.687      -0.200       0.131
ar.L21        -0.1984      0.007    -29.951      0.000      -0.211      -0.185
ar.L22        -0.0506      0.034     -1.468      0.142      -0.118       0.017
ar.L23        -0.0741      0.014     -5.353      0.000      -0.101      -0.047
ar.L24        -0.0036      0.016     -0.228      0.820      -0.034       0.027
ar.L25        -0.0581      0.007     -7.879      0.000      -0.073      -0.044
ar.L26         0.0126      0.013      0.989      0.323      -0.012       0.038
ar.L27        -0.0269      0.008     -3.570      0.000      -0.042      -0.012
ar.L28         0.0225      0.009      2.596      0.009       0.006       0.039
ar.L29        -0.0424      0.008     -5.254      0.000      -0.058      -0.027
ar.L30         0.0426      0.013      3.342      0.001       0.018       0.068
ma.L1         -0.1061      0.173     -0.613      0.540      -0.445       0.233
sigma2        26.1201      0.120    216.975      0.000      25.884      26.356
===================================================================================
Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):            123484.84
Prob(Q):                              0.93   Prob(JB):                         0.00
Heteroskedasticity (H):             198.84   Skew:                            -0.10
Prob(H) (two-sided):                  0.00   Kurtosis:                        15.42
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19222    779.873428
19223    778.040535
19224    775.839863
19225    777.674855
19226    777.127177
19227    779.222758
19228    778.124548
19229    779.738985
19230    777.842032
19231    778.299331
Name: predicted_mean, dtype: float64
       lower price  upper price
19222   769.856475   789.890381
19223   767.939937   788.141134
19224   763.458765   788.220961
19225   764.970317   790.379393
19226   763.001140   791.253214
19227   764.793457   793.652059
19228   762.474962   793.774134
19229   763.801615   795.676356
19230   760.766077   794.917987
19231   760.993159   795.605504
                            price short_name
timestamp                                   
2021-12-27 09:00:00+03:00   77.30      GUBRF
2021-12-27 10:00:00+03:00   81.00      GUBRF
2021-12-27 11:00:00+03:00   81.15      GUBRF
2021-12-27 12:00:00+03:00   82.35      GUBRF
2021-12-27 13:00:00+03:00   83.00      GUBRF
...                           ...        ...
2024-01-11 14:00:00+03:00  141.90      GUBRF
2024-01-11 15:00:00+03:00  140.90      GUBRF
2024-01-11 16:00:00+03:00  141.10      GUBRF
2024-01-11 17:00:00+03:00  141.00      GUBRF
2024-01-11 18:00:00+03:00  141.10      GUBRF

[5103 rows x 2 columns]

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    77.30
2021-12-27 07:00:00+00:00    81.00
2021-12-27 08:00:00+00:00    81.15
2021-12-27 09:00:00+00:00    82.35
2021-12-27 10:00:00+00:00    83.00
2021-12-27 11:00:00+00:00    82.50
2021-12-27 12:00:00+00:00    83.15
2021-12-27 13:00:00+00:00    83.10
2021-12-27 14:00:00+00:00    83.15
2021-12-27 15:00:00+00:00    83.50
2021-12-28 06:00:00+00:00    85.60
2021-12-28 07:00:00+00:00    84.25
2021-12-28 08:00:00+00:00    82.85
2021-12-28 09:00:00+00:00    82.90
2021-12-28 10:00:00+00:00    83.50
2021-12-28 11:00:00+00:00    83.95
2021-12-28 12:00:00+00:00    83.60
2021-12-28 13:00:00+00:00    83.00
2021-12-28 14:00:00+00:00    81.85
2021-12-28 15:00:00+00:00    81.85
2021-12-29 06:00:00+00:00    81.90
2021-12-29 07:00:00+00:00    81.35
2021-12-29 08:00:00+00:00    82.00
2021-12-29 09:00:00+00:00    81.80
2021-12-29 10:00:00+00:00    82.00
2021-12-29 11:00:00+00:00    81.65
2021-12-29 12:00:00+00:00    81.80
2021-12-29 13:00:00+00:00    81.90
2021-12-29 14:00:00+00:00    81.40
2021-12-29 15:00:00+00:00    81.20
2021-12-30 06:00:00+00:00    82.05
2021-12-30 07:00:00+00:00    81.30
2021-12-30 08:00:00+00:00    81.25
2021-12-30 09:00:00+00:00    81.55
2021-12-30 10:00:00+00:00    82.05
2021-12-30 11:00:00+00:00    80.95
2021-12-30 12:00:00+00:00    80.95
2021-12-30 13:00:00+00:00    80.65
2021-12-30 14:00:00+00:00    78.65
2021-12-30 15:00:00+00:00    78.50
2021-12-31 06:00:00+00:00    79.15
2021-12-31 07:00:00+00:00    77.80
2021-12-31 08:00:00+00:00    77.60
2021-12-31 09:00:00+00:00    77.75
2021-12-31 10:00:00+00:00    77.35
2021-12-31 11:00:00+00:00    77.35
2021-12-31 12:00:00+00:00    77.20
2021-12-31 13:00:00+00:00    76.95
2021-12-31 14:00:00+00:00    76.70
2021-12-31 15:00:00+00:00    76.85
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    146.899994
2024-01-10 10:00:00+00:00    146.600000
2024-01-10 10:30:00+00:00    146.699997
2024-01-10 11:00:00+00:00    146.000000
2024-01-10 11:30:00+00:00    145.800003
2024-01-10 12:00:00+00:00    144.600000
2024-01-10 12:30:00+00:00    144.600006
2024-01-10 13:00:00+00:00    144.500000
2024-01-10 13:30:00+00:00    144.899994
2024-01-10 14:00:00+00:00    144.300000
2024-01-10 15:00:00+00:00    144.100000
2024-01-11 06:00:00+00:00    145.000000
2024-01-11 06:30:00+00:00    146.600006
2024-01-11 07:00:00+00:00    144.800000
2024-01-11 07:30:00+00:00    144.000000
2024-01-11 08:00:00+00:00    143.400000
2024-01-11 08:30:00+00:00    143.699997
2024-01-11 09:00:00+00:00    143.600000
2024-01-11 09:30:00+00:00    143.300003
2024-01-11 10:00:00+00:00    142.700000
2024-01-11 10:30:00+00:00    142.399994
2024-01-11 11:00:00+00:00    141.900000
2024-01-11 11:30:00+00:00    141.600006
2024-01-11 12:00:00+00:00    140.900000
2024-01-11 12:30:00+00:00    142.300003
2024-01-11 13:00:00+00:00    141.100000
2024-01-11 13:30:00+00:00    140.899994
2024-01-11 14:00:00+00:00    141.000000
2024-01-11 14:30:00+00:00    141.100006
2024-01-11 15:00:00+00:00    141.100000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00   -0.010000
2018-01-02 11:00:00+03:00    0.030000
2018-01-02 12:00:00+03:00    0.030000
2018-01-02 13:00:00+03:00    0.010000
2018-01-02 14:00:00+03:00    0.030000
                               ...   
2024-01-11 13:00:00+00:00   -1.200003
2024-01-11 13:30:00+00:00   -0.200006
2024-01-11 14:00:00+00:00    0.100006
2024-01-11 14:30:00+00:00    0.100006
2024-01-11 15:00:00+00:00   -0.000006
Name: price, Length: 19212, dtype: float64
ADF Statistic: -18.2075741124874
p-value: 2.3972053326912787e-30
Critical Values: {'1%': -3.430691238445059, '5%': -2.861690815038142, '10%': -2.566850274786686}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19213
Model:                ARIMA(30, 3, 1)   Log Likelihood              -33276.763
Date:                Thu, 11 Jan 2024   AIC                          66617.526
Time:                        21:34:47   BIC                          66869.148
Sample:                             0   HQIC                         66700.009
                              - 19213                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.9827      0.004   -274.075      0.000      -0.990      -0.976
ar.L2         -0.9423      0.005   -184.673      0.000      -0.952      -0.932
ar.L3         -0.9073      0.007   -137.582      0.000      -0.920      -0.894
ar.L4         -0.8842      0.008   -111.932      0.000      -0.900      -0.869
ar.L5         -0.8770      0.008   -105.052      0.000      -0.893      -0.861
ar.L6         -0.8649      0.009    -92.309      0.000      -0.883      -0.846
ar.L7         -0.8396      0.010    -82.356      0.000      -0.860      -0.820
ar.L8         -0.8088      0.011    -73.625      0.000      -0.830      -0.787
ar.L9         -0.7733      0.011    -67.301      0.000      -0.796      -0.751
ar.L10        -0.7636      0.012    -64.763      0.000      -0.787      -0.741
ar.L11        -0.7212      0.012    -58.499      0.000      -0.745      -0.697
ar.L12        -0.6781      0.012    -55.049      0.000      -0.702      -0.654
ar.L13        -0.6363      0.013    -50.574      0.000      -0.661      -0.612
ar.L14        -0.6017      0.012    -48.692      0.000      -0.626      -0.577
ar.L15        -0.5776      0.012    -47.885      0.000      -0.601      -0.554
ar.L16        -0.5558      0.012    -47.325      0.000      -0.579      -0.533
ar.L17        -0.5157      0.011    -45.232      0.000      -0.538      -0.493
ar.L18        -0.3429      0.012    -28.855      0.000      -0.366      -0.320
ar.L19        -0.2985      0.012    -25.738      0.000      -0.321      -0.276
ar.L20        -0.2976      0.011    -26.712      0.000      -0.319      -0.276
ar.L21        -0.2967      0.011    -27.977      0.000      -0.317      -0.276
ar.L22        -0.2744      0.010    -26.831      0.000      -0.294      -0.254
ar.L23        -0.2165      0.010    -22.292      0.000      -0.236      -0.197
ar.L24        -0.1812      0.009    -19.970      0.000      -0.199      -0.163
ar.L25        -0.1473      0.009    -17.152      0.000      -0.164      -0.130
ar.L26        -0.1106      0.008    -13.581      0.000      -0.127      -0.095
ar.L27        -0.0990      0.007    -13.321      0.000      -0.114      -0.084
ar.L28        -0.0799      0.006    -12.929      0.000      -0.092      -0.068
ar.L29        -0.0932      0.005    -19.626      0.000      -0.103      -0.084
ar.L30        -0.0586      0.003    -22.610      0.000      -0.064      -0.053
ma.L1         -1.0000      0.006   -181.721      0.000      -1.011      -0.989
sigma2         1.8695      0.009    212.873      0.000       1.852       1.887
===================================================================================
Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):           3285170.73
Prob(Q):                              0.92   Prob(JB):                         0.00
Heteroskedasticity (H):             307.16   Skew:                            -1.92
Prob(H) (two-sided):                  0.00   Kurtosis:                        66.95
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19213    141.148688
19214    140.669771
19215    140.292672
19216    140.025225
19217    139.924207
19218    139.840951
19219    139.579083
19220    139.326880
19221    139.126220
19222    138.881193
Name: predicted_mean, dtype: float64
       lower price  upper price
19213   138.468773   143.828604
19214   136.846759   144.492782
19215   135.532861   145.052483
19216   134.434113   145.616337
19217   133.577602   146.270813
19218   132.807031   146.874870
19219   131.902417   147.255750
19220   131.028786   147.624974
19221   130.217149   148.035290
19222   129.363001   148.399385
                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00  10.6645      GARAN
2021-12-27 10:00:00+03:00  10.7373      GARAN
2021-12-27 11:00:00+03:00  10.7282      GARAN
2021-12-27 12:00:00+03:00  10.7282      GARAN
2021-12-27 13:00:00+03:00  10.7191      GARAN
...                            ...        ...
2024-01-11 14:00:00+03:00  66.0000      GARAN
2024-01-11 15:00:00+03:00  65.3500      GARAN
2024-01-11 16:00:00+03:00  65.5000      GARAN
2024-01-11 17:00:00+03:00  65.9000      GARAN
2024-01-11 18:00:00+03:00  65.6000      GARAN

[5104 rows x 2 columns]

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    10.6645
2021-12-27 07:00:00+00:00    10.7373
2021-12-27 08:00:00+00:00    10.7282
2021-12-27 09:00:00+00:00    10.7282
2021-12-27 10:00:00+00:00    10.7191
2021-12-27 11:00:00+00:00    10.6736
2021-12-27 12:00:00+00:00    10.6645
2021-12-27 13:00:00+00:00    10.6645
2021-12-27 14:00:00+00:00    10.6372
2021-12-27 15:00:00+00:00    10.6190
2021-12-28 06:00:00+00:00    10.6009
2021-12-28 07:00:00+00:00    10.5371
2021-12-28 08:00:00+00:00    10.5007
2021-12-28 09:00:00+00:00    10.4553
2021-12-28 10:00:00+00:00    10.4462
2021-12-28 11:00:00+00:00    10.4189
2021-12-28 12:00:00+00:00    10.4553
2021-12-28 13:00:00+00:00    10.4280
2021-12-28 14:00:00+00:00    10.2278
2021-12-28 15:00:00+00:00    10.2278
2021-12-29 06:00:00+00:00    10.1913
2021-12-29 07:00:00+00:00    10.2642
2021-12-29 08:00:00+00:00    10.4371
2021-12-29 09:00:00+00:00    10.4280
2021-12-29 10:00:00+00:00    10.4553
2021-12-29 11:00:00+00:00    10.4189
2021-12-29 12:00:00+00:00    10.4098
2021-12-29 13:00:00+00:00    10.4371
2021-12-29 14:00:00+00:00    10.3824
2021-12-29 15:00:00+00:00    10.4098
2021-12-30 06:00:00+00:00    10.4371
2021-12-30 07:00:00+00:00    10.5280
2021-12-30 08:00:00+00:00    10.5553
2021-12-30 09:00:00+00:00    10.5462
2021-12-30 10:00:00+00:00    10.5918
2021-12-30 11:00:00+00:00    10.5098
2021-12-30 12:00:00+00:00    10.5007
2021-12-30 13:00:00+00:00    10.5462
2021-12-30 14:00:00+00:00    10.4462
2021-12-30 15:00:00+00:00    10.4280
2021-12-31 06:00:00+00:00    10.3733
2021-12-31 07:00:00+00:00    10.3551
2021-12-31 08:00:00+00:00    10.3733
2021-12-31 09:00:00+00:00    10.3642
2021-12-31 10:00:00+00:00    10.3369
2021-12-31 11:00:00+00:00    10.3278
2021-12-31 12:00:00+00:00    10.2915
2021-12-31 13:00:00+00:00    10.2642
2021-12-31 14:00:00+00:00    10.2187
2021-12-31 15:00:00+00:00    10.2551
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    62.950001
2024-01-10 10:00:00+00:00    63.100000
2024-01-10 10:30:00+00:00    63.500000
2024-01-10 11:00:00+00:00    63.450000
2024-01-10 11:30:00+00:00    63.650002
2024-01-10 12:00:00+00:00    63.300000
2024-01-10 12:30:00+00:00    63.500000
2024-01-10 13:00:00+00:00    64.150000
2024-01-10 13:30:00+00:00    63.799999
2024-01-10 14:00:00+00:00    63.900000
2024-01-10 15:00:00+00:00    63.800000
2024-01-11 06:00:00+00:00    64.250000
2024-01-11 06:30:00+00:00    66.750000
2024-01-11 07:00:00+00:00    65.700000
2024-01-11 07:30:00+00:00    66.199997
2024-01-11 08:00:00+00:00    65.950000
2024-01-11 08:30:00+00:00    66.300003
2024-01-11 09:00:00+00:00    66.100000
2024-01-11 09:30:00+00:00    66.050003
2024-01-11 10:00:00+00:00    65.700000
2024-01-11 10:30:00+00:00    66.099998
2024-01-11 11:00:00+00:00    66.000000
2024-01-11 11:30:00+00:00    65.900002
2024-01-11 12:00:00+00:00    65.350000
2024-01-11 12:30:00+00:00    66.150002
2024-01-11 13:00:00+00:00    65.500000
2024-01-11 13:30:00+00:00    66.000000
2024-01-11 14:00:00+00:00    65.900000
2024-01-11 14:30:00+00:00    65.599998
2024-01-11 15:00:00+00:00    65.600000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    0.111000
2018-01-02 11:00:00+03:00    0.025700
2018-01-02 12:00:00+03:00   -0.017200
2018-01-02 13:00:00+03:00    0.008600
2018-01-02 14:00:00+03:00    0.008600
                               ...   
2024-01-11 13:00:00+00:00   -0.650002
2024-01-11 13:30:00+00:00    0.500000
2024-01-11 14:00:00+00:00   -0.100000
2024-01-11 14:30:00+00:00   -0.300002
2024-01-11 15:00:00+00:00    0.000002
Name: price, Length: 19223, dtype: float64
ADF Statistic: -20.50576228600906
p-value: 0.0
Critical Values: {'1%': -3.4306910248977847, '5%': -2.8616907206633995, '10%': -2.5668502245526077}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19224
Model:                ARIMA(30, 1, 1)   Log Likelihood               -4954.666
Date:                Thu, 11 Jan 2024   AIC                           9973.331
Time:                        21:37:31   BIC                          10224.975
Sample:                             0   HQIC                         10055.819
                              - 19224                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.2520      0.027    -45.696      0.000      -1.306      -1.198
ar.L2         -0.3905      0.019    -20.938      0.000      -0.427      -0.354
ar.L3         -0.0640      0.007     -9.714      0.000      -0.077      -0.051
ar.L4         -0.0424      0.007     -5.656      0.000      -0.057      -0.028
ar.L5         -0.0288      0.008     -3.774      0.000      -0.044      -0.014
ar.L6          0.0031      0.008      0.390      0.697      -0.012       0.018
ar.L7         -0.0116      0.008     -1.427      0.154      -0.027       0.004
ar.L8         -0.0108      0.009     -1.265      0.206      -0.028       0.006
ar.L9         -0.0264      0.009     -3.004      0.003      -0.044      -0.009
ar.L10         0.0134      0.009      1.470      0.142      -0.004       0.031
ar.L11        -0.0141      0.008     -1.670      0.095      -0.031       0.002
ar.L12        -0.0179      0.008     -2.251      0.024      -0.034      -0.002
ar.L13         0.0072      0.008      0.920      0.357      -0.008       0.022
ar.L14         0.0046      0.008      0.605      0.545      -0.010       0.020
ar.L15         0.0203      0.007      2.732      0.006       0.006       0.035
ar.L16        -0.0860      0.007    -12.474      0.000      -0.099      -0.072
ar.L17        -0.1255      0.006    -20.971      0.000      -0.137      -0.114
ar.L18         0.2068      0.006     35.548      0.000       0.195       0.218
ar.L19         0.5322      0.008     67.104      0.000       0.517       0.548
ar.L20         0.1014      0.012      8.784      0.000       0.079       0.124
ar.L21        -0.1844      0.007    -26.633      0.000      -0.198      -0.171
ar.L22        -0.0745      0.008     -9.655      0.000      -0.090      -0.059
ar.L23        -0.0724      0.008     -9.575      0.000      -0.087      -0.058
ar.L24        -0.0401      0.008     -4.921      0.000      -0.056      -0.024
ar.L25        -0.0303      0.008     -3.699      0.000      -0.046      -0.014
ar.L26         0.0008      0.008      0.097      0.923      -0.016       0.017
ar.L27        -0.0160      0.008     -1.892      0.058      -0.033       0.001
ar.L28        -0.0085      0.009     -0.943      0.346      -0.026       0.009
ar.L29         0.0054      0.008      0.643      0.520      -0.011       0.022
ar.L30         0.0614      0.007      9.038      0.000       0.048       0.075
ma.L1          0.6058      0.028     21.928      0.000       0.552       0.660
sigma2         0.0961      0.000    243.936      0.000       0.095       0.097
===================================================================================
Ljung-Box (L1) (Q):                   1.00   Jarque-Bera (JB):            193049.00
Prob(Q):                              0.32   Prob(JB):                         0.00
Heteroskedasticity (H):              41.43   Skew:                            -0.06
Prob(H) (two-sided):                  0.00   Kurtosis:                        18.52
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224    66.417542
19225    66.535853
19226    65.824443
19227    66.199842
19228    65.984873
19229    66.038206
19230    65.978039
19231    65.816572
19232    65.928920
19233    66.039502
Name: predicted_mean, dtype: float64
       lower price  upper price
19224    65.809818    67.025266
19225    65.891214    67.180492
19226    65.027024    66.621862
19227    65.359428    67.040256
19228    65.044980    66.924767
19229    65.054975    67.021436
19230    64.913175    67.042903
19231    64.712097    66.921047
19232    64.755054    67.102785
19233    64.830327    67.248676

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00   8.8236      KRDMD
2021-12-27 10:00:00+03:00   9.1310      KRDMD
2021-12-27 11:00:00+03:00   9.0565      KRDMD
2021-12-27 12:00:00+03:00   9.0472      KRDMD
2021-12-27 13:00:00+03:00   9.0006      KRDMD
...                            ...        ...
2024-01-11 14:00:00+03:00  25.8200      KRDMD
2024-01-11 15:00:00+03:00  25.5200      KRDMD
2024-01-11 16:00:00+03:00  25.6000      KRDMD
2024-01-11 17:00:00+03:00  25.7400      KRDMD
2024-01-11 18:00:00+03:00  25.8600      KRDMD

[5104 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    8.8236
2021-12-27 07:00:00+00:00    9.1310
2021-12-27 08:00:00+00:00    9.0565
2021-12-27 09:00:00+00:00    9.0472
2021-12-27 10:00:00+00:00    9.0006
2021-12-27 11:00:00+00:00    8.9075
2021-12-27 12:00:00+00:00    8.8329
2021-12-27 13:00:00+00:00    8.8329
2021-12-27 14:00:00+00:00    8.7489
2021-12-27 15:00:00+00:00    8.7397
2021-12-28 06:00:00+00:00    8.8702
2021-12-28 07:00:00+00:00    8.8049
2021-12-28 08:00:00+00:00    8.7770
2021-12-28 09:00:00+00:00    8.7584
2021-12-28 10:00:00+00:00    8.7304
2021-12-28 11:00:00+00:00    8.5813
2021-12-28 12:00:00+00:00    8.6373
2021-12-28 13:00:00+00:00    8.5627
2021-12-28 14:00:00+00:00    8.3857
2021-12-28 15:00:00+00:00    8.3764
2021-12-29 06:00:00+00:00    8.3671
2021-12-29 07:00:00+00:00    8.3483
2021-12-29 08:00:00+00:00    8.5721
2021-12-29 09:00:00+00:00    8.5999
2021-12-29 10:00:00+00:00    8.6559
2021-12-29 11:00:00+00:00    8.6373
2021-12-29 12:00:00+00:00    8.6186
2021-12-29 13:00:00+00:00    8.7025
2021-12-29 14:00:00+00:00    8.7118
2021-12-29 15:00:00+00:00    8.7304
2021-12-30 06:00:00+00:00    8.8889
2021-12-30 07:00:00+00:00    8.7863
2021-12-30 08:00:00+00:00    8.7211
2021-12-30 09:00:00+00:00    8.6279
2021-12-30 10:00:00+00:00    8.6092
2021-12-30 11:00:00+00:00    8.4509
2021-12-30 12:00:00+00:00    8.4975
2021-12-30 13:00:00+00:00    8.5721
2021-12-30 14:00:00+00:00    8.4881
2021-12-30 15:00:00+00:00    8.4788
2021-12-31 06:00:00+00:00    8.4788
2021-12-31 07:00:00+00:00    8.5068
2021-12-31 08:00:00+00:00    8.5721
2021-12-31 09:00:00+00:00    8.6092
2021-12-31 10:00:00+00:00    8.5348
2021-12-31 11:00:00+00:00    8.5534
2021-12-31 12:00:00+00:00    8.5440
2021-12-31 13:00:00+00:00    8.6092
2021-12-31 14:00:00+00:00    8.5254
2021-12-31 15:00:00+00:00    8.5906
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    25.360001
2024-01-10 10:00:00+00:00    25.340000
2024-01-10 10:30:00+00:00    25.379999
2024-01-10 11:00:00+00:00    25.320000
2024-01-10 11:30:00+00:00    25.240000
2024-01-10 12:00:00+00:00    25.160000
2024-01-10 12:30:00+00:00    25.360001
2024-01-10 13:00:00+00:00    25.480000
2024-01-10 13:30:00+00:00    25.459999
2024-01-10 14:00:00+00:00    25.460000
2024-01-10 15:00:00+00:00    25.420000
2024-01-11 06:00:00+00:00    25.440000
2024-01-11 06:30:00+00:00    25.719999
2024-01-11 07:00:00+00:00    25.600000
2024-01-11 07:30:00+00:00    25.540001
2024-01-11 08:00:00+00:00    25.660000
2024-01-11 08:30:00+00:00    25.719999
2024-01-11 09:00:00+00:00    25.720000
2024-01-11 09:30:00+00:00    25.680000
2024-01-11 10:00:00+00:00    25.700000
2024-01-11 10:30:00+00:00    25.680000
2024-01-11 11:00:00+00:00    25.820000
2024-01-11 11:30:00+00:00    25.719999
2024-01-11 12:00:00+00:00    25.520000
2024-01-11 12:30:00+00:00    25.799999
2024-01-11 13:00:00+00:00    25.600000
2024-01-11 13:30:00+00:00    25.680000
2024-01-11 14:00:00+00:00    25.740000
2024-01-11 14:30:00+00:00    25.860001
2024-01-11 15:00:00+00:00    25.860000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00   -1.660000e-02
2018-01-02 11:00:00+03:00    4.150000e-02
2018-01-02 12:00:00+03:00   -2.490000e-02
2018-01-02 13:00:00+03:00    0.000000e+00
2018-01-02 14:00:00+03:00    8.300000e-03
                                 ...     
2024-01-11 13:00:00+00:00   -1.999992e-01
2024-01-11 13:30:00+00:00    8.000031e-02
2024-01-11 14:00:00+00:00    5.999969e-02
2024-01-11 14:30:00+00:00    1.200006e-01
2024-01-11 15:00:00+00:00   -6.103516e-07
Name: price, Length: 19223, dtype: float64
ADF Statistic: -20.545140810318422
p-value: 0.0
Critical Values: {'1%': -3.4306910426831823, '5%': -2.86169072852345, '10%': -2.5668502287363797}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19224
Model:                ARIMA(30, 3, 1)   Log Likelihood                8618.516
Date:                Thu, 11 Jan 2024   AIC                         -17173.031
Time:                        21:40:30   BIC                         -16921.391
Sample:                             0   HQIC                        -17090.544
                              - 19224                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.4046      0.004   -336.622      0.000      -1.413      -1.396
ar.L2         -1.2413      0.009   -142.237      0.000      -1.258      -1.224
ar.L3         -1.3032      0.012   -107.087      0.000      -1.327      -1.279
ar.L4         -1.1937      0.015    -78.192      0.000      -1.224      -1.164
ar.L5         -1.1650      0.018    -65.593      0.000      -1.200      -1.130
ar.L6         -1.0798      0.020    -54.172      0.000      -1.119      -1.041
ar.L7         -1.0365      0.022    -47.893      0.000      -1.079      -0.994
ar.L8         -0.9501      0.023    -41.993      0.000      -0.994      -0.906
ar.L9         -0.9275      0.023    -39.608      0.000      -0.973      -0.882
ar.L10        -0.8871      0.024    -36.335      0.000      -0.935      -0.839
ar.L11        -0.8606      0.025    -34.093      0.000      -0.910      -0.811
ar.L12        -0.8613      0.026    -33.241      0.000      -0.912      -0.810
ar.L13        -0.7716      0.026    -29.415      0.000      -0.823      -0.720
ar.L14        -0.7490      0.026    -28.464      0.000      -0.801      -0.697
ar.L15        -0.7171      0.026    -27.147      0.000      -0.769      -0.665
ar.L16        -0.7547      0.026    -28.891      0.000      -0.806      -0.703
ar.L17        -0.8116      0.026    -31.584      0.000      -0.862      -0.761
ar.L18        -0.4588      0.026    -17.854      0.000      -0.509      -0.408
ar.L19        -0.2520      0.025    -10.120      0.000      -0.301      -0.203
ar.L20        -0.4038      0.024    -16.860      0.000      -0.451      -0.357
ar.L21        -0.3635      0.023    -15.552      0.000      -0.409      -0.318
ar.L22        -0.3596      0.022    -16.067      0.000      -0.403      -0.316
ar.L23        -0.3238      0.022    -14.840      0.000      -0.367      -0.281
ar.L24        -0.2615      0.020    -13.026      0.000      -0.301      -0.222
ar.L25        -0.2370      0.019    -12.739      0.000      -0.273      -0.201
ar.L26        -0.1714      0.017    -10.075      0.000      -0.205      -0.138
ar.L27        -0.1441      0.015     -9.356      0.000      -0.174      -0.114
ar.L28        -0.0515      0.013     -4.023      0.000      -0.077      -0.026
ar.L29        -0.0410      0.010     -4.163      0.000      -0.060      -0.022
ar.L30         0.0559      0.006      9.099      0.000       0.044       0.068
ma.L1         -0.9712      0.003   -359.666      0.000      -0.977      -0.966
sigma2         0.0242      0.000    236.896      0.000       0.024       0.024
===================================================================================
Ljung-Box (L1) (Q):                   3.31   Jarque-Bera (JB):            249797.70
Prob(Q):                              0.07   Prob(JB):                         0.00
Heteroskedasticity (H):              50.60   Skew:                             0.26
Prob(H) (two-sided):                  0.00   Kurtosis:                        20.65
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224    26.003483
19225    25.945138
19226    25.943732
19227    25.970318
19228    26.084576
19229    26.063838
19230    26.114262
19231    26.113155
19232    26.143461
19233    26.213951
Name: predicted_mean, dtype: float64
       lower price  upper price
19224    25.698528    26.308438
19225    25.585649    26.304627
19226    25.478508    26.408955
19227    25.453973    26.486664
19228    25.477502    26.691651
19229    25.400418    26.727258
19230    25.363525    26.865000
19231    25.300716    26.925595
19232    25.241621    27.045301
19233    25.247232    27.180671

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

                              price short_name
timestamp                                     
2021-12-27 09:00:00+03:00   28.3315      KCHOL
2021-12-27 10:00:00+03:00   28.7712      KCHOL
2021-12-27 11:00:00+03:00   28.6565      KCHOL
2021-12-27 12:00:00+03:00   28.6375      KCHOL
2021-12-27 13:00:00+03:00   28.6565      KCHOL
...                             ...        ...
2024-01-11 14:00:00+03:00  150.0000      KCHOL
2024-01-11 15:00:00+03:00  150.8000      KCHOL
2024-01-11 16:00:00+03:00  152.9000      KCHOL
2024-01-11 17:00:00+03:00  153.2000      KCHOL
2024-01-11 18:00:00+03:00  153.2000      KCHOL

[5104 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    28.3315
2021-12-27 07:00:00+00:00    28.7712
2021-12-27 08:00:00+00:00    28.6565
2021-12-27 09:00:00+00:00    28.6375
2021-12-27 10:00:00+00:00    28.6565
2021-12-27 11:00:00+00:00    28.2360
2021-12-27 12:00:00+00:00    28.4272
2021-12-27 13:00:00+00:00    28.4654
2021-12-27 14:00:00+00:00    28.1977
2021-12-27 15:00:00+00:00    28.1404
2021-12-28 06:00:00+00:00    28.2550
2021-12-28 07:00:00+00:00    28.0639
2021-12-28 08:00:00+00:00    27.7389
2021-12-28 09:00:00+00:00    27.6815
2021-12-28 10:00:00+00:00    27.7198
2021-12-28 11:00:00+00:00    27.5477
2021-12-28 12:00:00+00:00    27.6433
2021-12-28 13:00:00+00:00    27.3757
2021-12-28 14:00:00+00:00    27.1845
2021-12-28 15:00:00+00:00    27.4330
2021-12-29 06:00:00+00:00    27.3184
2021-12-29 07:00:00+00:00    27.0316
2021-12-29 08:00:00+00:00    27.6051
2021-12-29 09:00:00+00:00    27.7198
2021-12-29 10:00:00+00:00    27.7198
2021-12-29 11:00:00+00:00    27.7772
2021-12-29 12:00:00+00:00    28.0257
2021-12-29 13:00:00+00:00    28.2550
2021-12-29 14:00:00+00:00    28.1595
2021-12-29 15:00:00+00:00    28.1595
2021-12-30 06:00:00+00:00    28.3698
2021-12-30 07:00:00+00:00    28.0065
2021-12-30 08:00:00+00:00    28.1977
2021-12-30 09:00:00+00:00    28.1977
2021-12-30 10:00:00+00:00    28.1022
2021-12-30 11:00:00+00:00    27.7963
2021-12-30 12:00:00+00:00    27.8154
2021-12-30 13:00:00+00:00    27.8537
2021-12-30 14:00:00+00:00    27.5477
2021-12-30 15:00:00+00:00    27.4330
2021-12-31 06:00:00+00:00    27.4522
2021-12-31 07:00:00+00:00    27.4139
2021-12-31 08:00:00+00:00    27.7580
2021-12-31 09:00:00+00:00    27.7963
2021-12-31 10:00:00+00:00    27.3757
2021-12-31 11:00:00+00:00    27.4330
2021-12-31 12:00:00+00:00    27.3565
2021-12-31 13:00:00+00:00    27.3565
2021-12-31 14:00:00+00:00    27.1272
2021-12-31 15:00:00+00:00    27.1272
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    144.699997
2024-01-10 10:00:00+00:00    144.400000
2024-01-10 10:30:00+00:00    144.600006
2024-01-10 11:00:00+00:00    144.800000
2024-01-10 11:30:00+00:00    144.800003
2024-01-10 12:00:00+00:00    144.600000
2024-01-10 12:30:00+00:00    146.300003
2024-01-10 13:00:00+00:00    146.900000
2024-01-10 13:30:00+00:00    146.800003
2024-01-10 14:00:00+00:00    147.600000
2024-01-10 15:00:00+00:00    147.100000
2024-01-11 06:00:00+00:00    147.400000
2024-01-11 06:30:00+00:00    147.500000
2024-01-11 07:00:00+00:00    146.900000
2024-01-11 07:30:00+00:00    147.100006
2024-01-11 08:00:00+00:00    147.100000
2024-01-11 08:30:00+00:00    149.300003
2024-01-11 09:00:00+00:00    150.100000
2024-01-11 09:30:00+00:00    150.199997
2024-01-11 10:00:00+00:00    150.200000
2024-01-11 10:30:00+00:00    150.100006
2024-01-11 11:00:00+00:00    150.000000
2024-01-11 11:30:00+00:00    150.899994
2024-01-11 12:00:00+00:00    150.800000
2024-01-11 12:30:00+00:00    152.600006
2024-01-11 13:00:00+00:00    152.900000
2024-01-11 13:30:00+00:00    152.699997
2024-01-11 14:00:00+00:00    153.200000
2024-01-11 14:30:00+00:00    153.199997
2024-01-11 15:00:00+00:00    153.200000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00   -0.026200
2018-01-02 11:00:00+03:00    0.252400
2018-01-02 12:00:00+03:00   -0.087000
2018-01-02 13:00:00+03:00    0.078200
2018-01-02 14:00:00+03:00    0.130500
                               ...   
2024-01-11 13:00:00+00:00    0.299994
2024-01-11 13:30:00+00:00   -0.200003
2024-01-11 14:00:00+00:00    0.500003
2024-01-11 14:30:00+00:00   -0.000003
2024-01-11 15:00:00+00:00    0.000003
Name: price, Length: 19222, dtype: float64
ADF Statistic: -21.73931530065581
p-value: 0.0
Critical Values: {'1%': -3.4306910604704344, '5%': -2.86169073638432, '10%': -2.566850232920588}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19223
Model:                ARIMA(30, 3, 1)   Log Likelihood              -13379.376
Date:                Thu, 11 Jan 2024   AIC                          26822.752
Time:                        21:45:41   BIC                          27074.391
Sample:                             0   HQIC                         26905.239
                              - 19223                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.2840      0.003   -380.301      0.000      -1.291      -1.277
ar.L2         -1.1421      0.005   -209.057      0.000      -1.153      -1.131
ar.L3         -1.2198      0.007   -168.596      0.000      -1.234      -1.206
ar.L4         -1.0992      0.009   -126.801      0.000      -1.116      -1.082
ar.L5         -1.1005      0.010   -111.528      0.000      -1.120      -1.081
ar.L6         -0.9938      0.011    -89.698      0.000      -1.016      -0.972
ar.L7         -0.9809      0.012    -80.628      0.000      -1.005      -0.957
ar.L8         -0.9312      0.013    -74.047      0.000      -0.956      -0.907
ar.L9         -0.9241      0.013    -71.888      0.000      -0.949      -0.899
ar.L10        -0.8305      0.013    -62.355      0.000      -0.857      -0.804
ar.L11        -0.7711      0.013    -57.628      0.000      -0.797      -0.745
ar.L12        -0.7363      0.013    -56.129      0.000      -0.762      -0.711
ar.L13        -0.6896      0.013    -53.057      0.000      -0.715      -0.664
ar.L14        -0.6714      0.013    -52.839      0.000      -0.696      -0.647
ar.L15        -0.6111      0.012    -49.267      0.000      -0.635      -0.587
ar.L16        -0.5982      0.012    -50.102      0.000      -0.622      -0.575
ar.L17        -0.5834      0.011    -51.638      0.000      -0.606      -0.561
ar.L18        -0.4119      0.011    -37.965      0.000      -0.433      -0.391
ar.L19        -0.3230      0.011    -29.736      0.000      -0.344      -0.302
ar.L20        -0.3872      0.010    -37.365      0.000      -0.407      -0.367
ar.L21        -0.3573      0.009    -37.803      0.000      -0.376      -0.339
ar.L22        -0.3529      0.009    -40.124      0.000      -0.370      -0.336
ar.L23        -0.3146      0.009    -36.404      0.000      -0.332      -0.298
ar.L24        -0.2586      0.007    -35.239      0.000      -0.273      -0.244
ar.L25        -0.2140      0.006    -36.515      0.000      -0.225      -0.203
ar.L26        -0.1037      0.004    -25.008      0.000      -0.112      -0.096
ar.L27        -0.0837      0.003    -26.720      0.000      -0.090      -0.078
ar.L28        -0.0288      0.005     -6.034      0.000      -0.038      -0.019
ar.L29        -0.0571      0.005    -11.855      0.000      -0.067      -0.048
ar.L30         0.0315      0.004      8.174      0.000       0.024       0.039
ma.L1         -0.9998      0.001   -680.739      0.000      -1.003      -0.997
sigma2         0.2356      0.001    271.802      0.000       0.234       0.237
===================================================================================
Ljung-Box (L1) (Q):                   0.22   Jarque-Bera (JB):            403118.56
Prob(Q):                              0.64   Prob(JB):                         0.00
Heteroskedasticity (H):              52.23   Skew:                            -0.10
Prob(H) (two-sided):                  0.00   Kurtosis:                        25.44
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223    153.669634
19224    153.731560
19225    154.243594
19226    154.417231
19227    155.152393
19228    155.514859
19229    155.724208
19230    155.943472
19231    156.057993
19232    156.363806
Name: predicted_mean, dtype: float64
       lower price  upper price
19223   152.718388   154.620880
19224   152.561533   154.901588
19225   152.771685   155.715502
19226   152.778294   156.056168
19227   153.265153   157.039633
19228   153.465040   157.564677
19229   153.439047   158.009368
19230   153.495781   158.391162
19231   153.395452   158.720535
19232   153.545049   159.182563

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00   4.9696      KOZAL
2021-12-27 10:00:00+03:00   5.0971      KOZAL
2021-12-27 11:00:00+03:00   5.0716      KOZAL
2021-12-27 12:00:00+03:00   5.0504      KOZAL
2021-12-27 13:00:00+03:00   5.0716      KOZAL
...                            ...        ...
2024-01-11 14:00:00+03:00  20.1800      KOZAL
2024-01-11 15:00:00+03:00  19.9400      KOZAL
2024-01-11 16:00:00+03:00  19.9300      KOZAL
2024-01-11 17:00:00+03:00  19.9300      KOZAL
2024-01-11 18:00:00+03:00  19.9100      KOZAL

[5104 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    4.9696
2021-12-27 07:00:00+00:00    5.0971
2021-12-27 08:00:00+00:00    5.0716
2021-12-27 09:00:00+00:00    5.0504
2021-12-27 10:00:00+00:00    5.0716
2021-12-27 11:00:00+00:00    5.0334
2021-12-27 12:00:00+00:00    5.0334
2021-12-27 13:00:00+00:00    5.0504
2021-12-27 14:00:00+00:00    4.9952
2021-12-27 15:00:00+00:00    5.0121
2021-12-28 06:00:00+00:00    5.0802
2021-12-28 07:00:00+00:00    5.1439
2021-12-28 08:00:00+00:00    5.0887
2021-12-28 09:00:00+00:00    5.0887
2021-12-28 10:00:00+00:00    5.0971
2021-12-28 11:00:00+00:00    5.0547
2021-12-28 12:00:00+00:00    5.1355
2021-12-28 13:00:00+00:00    5.1057
2021-12-28 14:00:00+00:00    5.0716
2021-12-28 15:00:00+00:00    5.0844
2021-12-29 06:00:00+00:00    5.0844
2021-12-29 07:00:00+00:00    4.9994
2021-12-29 08:00:00+00:00    5.0207
2021-12-29 09:00:00+00:00    5.0249
2021-12-29 10:00:00+00:00    5.0207
2021-12-29 11:00:00+00:00    5.0207
2021-12-29 12:00:00+00:00    5.0376
2021-12-29 13:00:00+00:00    5.0674
2021-12-29 14:00:00+00:00    5.0419
2021-12-29 15:00:00+00:00    5.0504
2021-12-30 06:00:00+00:00    5.1227
2021-12-30 07:00:00+00:00    5.0547
2021-12-30 08:00:00+00:00    5.0589
2021-12-30 09:00:00+00:00    5.0547
2021-12-30 10:00:00+00:00    5.0461
2021-12-30 11:00:00+00:00    4.9696
2021-12-30 12:00:00+00:00    4.9696
2021-12-30 13:00:00+00:00    4.9866
2021-12-30 14:00:00+00:00    4.9399
2021-12-30 15:00:00+00:00    4.9101
2021-12-31 06:00:00+00:00    4.9314
2021-12-31 07:00:00+00:00    4.9399
2021-12-31 08:00:00+00:00    4.9739
2021-12-31 09:00:00+00:00    4.9866
2021-12-31 10:00:00+00:00    4.9568
2021-12-31 11:00:00+00:00    4.9739
2021-12-31 12:00:00+00:00    4.9526
2021-12-31 13:00:00+00:00    4.9612
2021-12-31 14:00:00+00:00    4.9228
2021-12-31 15:00:00+00:00    4.9186
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    19.530001
2024-01-10 10:00:00+00:00    19.540000
2024-01-10 10:30:00+00:00    19.530001
2024-01-10 11:00:00+00:00    19.560000
2024-01-10 11:30:00+00:00    19.549999
2024-01-10 12:00:00+00:00    19.410000
2024-01-10 12:30:00+00:00    19.549999
2024-01-10 13:00:00+00:00    19.640000
2024-01-10 13:30:00+00:00    19.680000
2024-01-10 14:00:00+00:00    19.650000
2024-01-10 15:00:00+00:00    19.600000
2024-01-11 06:00:00+00:00    19.690000
2024-01-11 06:30:00+00:00    19.990000
2024-01-11 07:00:00+00:00    19.940000
2024-01-11 07:30:00+00:00    20.080000
2024-01-11 08:00:00+00:00    20.060000
2024-01-11 08:30:00+00:00    20.080000
2024-01-11 09:00:00+00:00    20.060000
2024-01-11 09:30:00+00:00    20.059999
2024-01-11 10:00:00+00:00    20.140000
2024-01-11 10:30:00+00:00    20.040001
2024-01-11 11:00:00+00:00    20.180000
2024-01-11 11:30:00+00:00    20.059999
2024-01-11 12:00:00+00:00    19.940000
2024-01-11 12:30:00+00:00    20.120001
2024-01-11 13:00:00+00:00    19.930000
2024-01-11 13:30:00+00:00    19.910000
2024-01-11 14:00:00+00:00    19.930000
2024-01-11 14:30:00+00:00    19.910000
2024-01-11 15:00:00+00:00    19.910000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    2.040000e-02
2018-01-02 11:00:00+03:00   -5.100000e-03
2018-01-02 12:00:00+03:00   -3.400000e-03
2018-01-02 13:00:00+03:00    0.000000e+00
2018-01-02 14:00:00+03:00    1.440000e-02
                                 ...     
2024-01-11 13:00:00+00:00   -1.900008e-01
2024-01-11 13:30:00+00:00   -2.000015e-02
2024-01-11 14:00:00+00:00    2.000015e-02
2024-01-11 14:30:00+00:00   -2.000015e-02
2024-01-11 15:00:00+00:00    1.525879e-07
Name: price, Length: 19222, dtype: float64
ADF Statistic: -19.90380801842556
p-value: 0.0
Critical Values: {'1%': -3.4306910604704344, '5%': -2.86169073638432, '10%': -2.566850232920588}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19223
Model:                ARIMA(30, 1, 1)   Log Likelihood              -25713.522
Date:                Thu, 11 Jan 2024   AIC                          51491.043
Time:                        21:48:15   BIC                          51742.685
Sample:                             0   HQIC                         51573.531
                              - 19223                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.6068      0.031    -19.786      0.000      -0.667      -0.547
ar.L2          0.3416      0.045      7.550      0.000       0.253       0.430
ar.L3          0.1720      0.029      5.963      0.000       0.115       0.229
ar.L4          0.0326      0.019      1.734      0.083      -0.004       0.070
ar.L5         -0.0080      0.015     -0.523      0.601      -0.038       0.022
ar.L6          0.0396      0.015      2.734      0.006       0.011       0.068
ar.L7         -0.0124      0.011     -1.121      0.262      -0.034       0.009
ar.L8          0.0011      0.010      0.109      0.913      -0.019       0.021
ar.L9         -0.0227      0.010     -2.196      0.028      -0.043      -0.002
ar.L10         0.0100      0.010      0.973      0.330      -0.010       0.030
ar.L11        -0.0297      0.008     -3.848      0.000      -0.045      -0.015
ar.L12        -0.0997      0.008    -12.564      0.000      -0.115      -0.084
ar.L13        -0.0548      0.010     -5.579      0.000      -0.074      -0.036
ar.L14        -0.0697      0.010     -7.237      0.000      -0.089      -0.051
ar.L15        -0.0197      0.010     -1.970      0.049      -0.039      -0.000
ar.L16        -0.1132      0.009    -11.918      0.000      -0.132      -0.095
ar.L17         0.0120      0.011      1.047      0.295      -0.010       0.035
ar.L18         0.7733      0.009     83.123      0.000       0.755       0.792
ar.L19         0.5639      0.016     34.689      0.000       0.532       0.596
ar.L20        -0.3612      0.032    -11.467      0.000      -0.423      -0.299
ar.L21        -0.2728      0.016    -16.592      0.000      -0.305      -0.241
ar.L22        -0.0518      0.006     -8.530      0.000      -0.064      -0.040
ar.L23        -0.0647      0.004    -16.134      0.000      -0.073      -0.057
ar.L24        -0.0522      0.004    -13.836      0.000      -0.060      -0.045
ar.L25        -0.0632      0.004    -16.450      0.000      -0.071      -0.056
ar.L26        -0.0179      0.006     -3.027      0.002      -0.029      -0.006
ar.L27        -0.0496      0.007     -7.099      0.000      -0.063      -0.036
ar.L28        -0.0266      0.007     -3.660      0.000      -0.041      -0.012
ar.L29        -0.0468      0.008     -5.989      0.000      -0.062      -0.031
ar.L30         0.0716      0.008      8.756      0.000       0.056       0.088
ma.L1         -0.8592      0.031    -27.744      0.000      -0.920      -0.798
sigma2         0.8525      0.002    434.362      0.000       0.849       0.856
===================================================================================
Ljung-Box (L1) (Q):                   0.34   Jarque-Bera (JB):          31488361.51
Prob(Q):                              0.56   Prob(JB):                         0.00
Heteroskedasticity (H):             736.06   Skew:                             5.35
Prob(H) (two-sided):                  0.00   Kurtosis:                       200.99
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223    20.183358
19224    20.109466
19225    20.195960
19226    20.148100
19227    20.168284
19228    20.071207
19229    20.123237
19230    20.102934
19231    20.103833
19232    20.112971
Name: predicted_mean, dtype: float64
       lower price  upper price
19223    18.373687    21.993028
19224    18.112949    22.105983
19225    17.766214    22.625706
19226    17.654155    22.642045
19227    17.487597    22.848971
19228    17.369366    22.773049
19229    17.299027    22.947447
19230    17.265318    22.940550
19231    17.169092    23.038573
19232    17.167400    23.058542
                           price short_name
timestamp                                  
2021-12-27 09:00:00+03:00  17.76      KOZAA
2021-12-27 10:00:00+03:00  18.25      KOZAA
2021-12-27 11:00:00+03:00  18.04      KOZAA
2021-12-27 12:00:00+03:00  17.92      KOZAA
2021-12-27 13:00:00+03:00  18.14      KOZAA
...                          ...        ...
2024-01-11 14:00:00+03:00  43.94      KOZAA
2024-01-11 15:00:00+03:00  43.58      KOZAA
2024-01-11 16:00:00+03:00  43.60      KOZAA
2024-01-11 17:00:00+03:00  43.62      KOZAA
2024-01-11 18:00:00+03:00  43.54      KOZAA

[5104 rows x 2 columns]

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    17.76
2021-12-27 07:00:00+00:00    18.25
2021-12-27 08:00:00+00:00    18.04
2021-12-27 09:00:00+00:00    17.92
2021-12-27 10:00:00+00:00    18.14
2021-12-27 11:00:00+00:00    18.11
2021-12-27 12:00:00+00:00    18.00
2021-12-27 13:00:00+00:00    18.04
2021-12-27 14:00:00+00:00    17.93
2021-12-27 15:00:00+00:00    17.94
2021-12-28 06:00:00+00:00    18.16
2021-12-28 07:00:00+00:00    18.33
2021-12-28 08:00:00+00:00    18.06
2021-12-28 09:00:00+00:00    18.06
2021-12-28 10:00:00+00:00    18.04
2021-12-28 11:00:00+00:00    17.91
2021-12-28 12:00:00+00:00    18.20
2021-12-28 13:00:00+00:00    18.00
2021-12-28 14:00:00+00:00    17.72
2021-12-28 15:00:00+00:00    17.77
2021-12-29 06:00:00+00:00    17.72
2021-12-29 07:00:00+00:00    17.57
2021-12-29 08:00:00+00:00    17.77
2021-12-29 09:00:00+00:00    17.77
2021-12-29 10:00:00+00:00    17.87
2021-12-29 11:00:00+00:00    17.84
2021-12-29 12:00:00+00:00    17.99
2021-12-29 13:00:00+00:00    18.11
2021-12-29 14:00:00+00:00    17.91
2021-12-29 15:00:00+00:00    17.91
2021-12-30 06:00:00+00:00    18.26
2021-12-30 07:00:00+00:00    18.05
2021-12-30 08:00:00+00:00    18.17
2021-12-30 09:00:00+00:00    18.13
2021-12-30 10:00:00+00:00    18.15
2021-12-30 11:00:00+00:00    17.86
2021-12-30 12:00:00+00:00    17.80
2021-12-30 13:00:00+00:00    17.93
2021-12-30 14:00:00+00:00    17.84
2021-12-30 15:00:00+00:00    17.74
2021-12-31 06:00:00+00:00    17.80
2021-12-31 07:00:00+00:00    17.74
2021-12-31 08:00:00+00:00    17.96
2021-12-31 09:00:00+00:00    18.03
2021-12-31 10:00:00+00:00    17.96
2021-12-31 11:00:00+00:00    18.04
2021-12-31 12:00:00+00:00    17.88
2021-12-31 13:00:00+00:00    18.05
2021-12-31 14:00:00+00:00    17.94
2021-12-31 15:00:00+00:00    18.02
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    42.119999
2024-01-10 10:00:00+00:00    42.160000
2024-01-10 10:30:00+00:00    42.160000
2024-01-10 11:00:00+00:00    42.340000
2024-01-10 11:30:00+00:00    42.400002
2024-01-10 12:00:00+00:00    42.000000
2024-01-10 12:30:00+00:00    42.380001
2024-01-10 13:00:00+00:00    42.600000
2024-01-10 13:30:00+00:00    42.700001
2024-01-10 14:00:00+00:00    42.660000
2024-01-10 15:00:00+00:00    42.660000
2024-01-11 06:00:00+00:00    42.720000
2024-01-11 06:30:00+00:00    43.400002
2024-01-11 07:00:00+00:00    43.500000
2024-01-11 07:30:00+00:00    44.040001
2024-01-11 08:00:00+00:00    43.940000
2024-01-11 08:30:00+00:00    43.680000
2024-01-11 09:00:00+00:00    43.560000
2024-01-11 09:30:00+00:00    43.599998
2024-01-11 10:00:00+00:00    43.740000
2024-01-11 10:30:00+00:00    43.700001
2024-01-11 11:00:00+00:00    43.940000
2024-01-11 11:30:00+00:00    43.820000
2024-01-11 12:00:00+00:00    43.580000
2024-01-11 12:30:00+00:00    43.980000
2024-01-11 13:00:00+00:00    43.600000
2024-01-11 13:30:00+00:00    43.599998
2024-01-11 14:00:00+00:00    43.620000
2024-01-11 14:30:00+00:00    43.540001
2024-01-11 15:00:00+00:00    43.540000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    2.000000e-02
2018-01-02 11:00:00+03:00   -3.000000e-02
2018-01-02 12:00:00+03:00   -2.000000e-02
2018-01-02 13:00:00+03:00    0.000000e+00
2018-01-02 14:00:00+03:00    2.000000e-02
                                 ...     
2024-01-11 13:00:00+00:00   -3.799995e-01
2024-01-11 13:30:00+00:00   -1.525879e-06
2024-01-11 14:00:00+00:00    2.000153e-02
2024-01-11 14:30:00+00:00   -7.999908e-02
2024-01-11 15:00:00+00:00   -9.155273e-07
Name: price, Length: 19222, dtype: float64
ADF Statistic: -19.704026393376765
p-value: 0.0
Critical Values: {'1%': -3.4306910426831823, '5%': -2.86169072852345, '10%': -2.5668502287363797}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19223
Model:                ARIMA(30, 3, 1)   Log Likelihood               -2897.723
Date:                Thu, 11 Jan 2024   AIC                           5859.446
Time:                        21:51:54   BIC                           6111.084
Sample:                             0   HQIC                          5941.933
                              - 19223                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.9624      0.003   -299.034      0.000      -0.969      -0.956
ar.L2         -0.9441      0.004   -212.302      0.000      -0.953      -0.935
ar.L3         -0.9047      0.006   -144.716      0.000      -0.917      -0.892
ar.L4         -0.8846      0.007   -122.883      0.000      -0.899      -0.870
ar.L5         -0.8490      0.008    -99.921      0.000      -0.866      -0.832
ar.L6         -0.8395      0.010    -86.966      0.000      -0.858      -0.821
ar.L7         -0.8068      0.010    -79.114      0.000      -0.827      -0.787
ar.L8         -0.7692      0.011    -72.573      0.000      -0.790      -0.748
ar.L9         -0.7254      0.011    -65.625      0.000      -0.747      -0.704
ar.L10        -0.6951      0.012    -58.019      0.000      -0.719      -0.672
ar.L11        -0.6487      0.012    -52.476      0.000      -0.673      -0.624
ar.L12        -0.6180      0.013    -48.410      0.000      -0.643      -0.593
ar.L13        -0.5918      0.013    -45.625      0.000      -0.617      -0.566
ar.L14        -0.5293      0.013    -40.251      0.000      -0.555      -0.503
ar.L15        -0.4989      0.013    -38.310      0.000      -0.524      -0.473
ar.L16        -0.4705      0.013    -36.351      0.000      -0.496      -0.445
ar.L17        -0.4228      0.013    -33.319      0.000      -0.448      -0.398
ar.L18        -0.3793      0.013    -30.215      0.000      -0.404      -0.355
ar.L19        -0.3552      0.012    -29.100      0.000      -0.379      -0.331
ar.L20        -0.3567      0.012    -30.254      0.000      -0.380      -0.334
ar.L21        -0.3614      0.012    -30.807      0.000      -0.384      -0.338
ar.L22        -0.3451      0.011    -30.643      0.000      -0.367      -0.323
ar.L23        -0.3020      0.011    -27.081      0.000      -0.324      -0.280
ar.L24        -0.2660      0.011    -24.733      0.000      -0.287      -0.245
ar.L25        -0.2289      0.010    -22.895      0.000      -0.248      -0.209
ar.L26        -0.1942      0.009    -21.381      0.000      -0.212      -0.176
ar.L27        -0.1609      0.008    -20.215      0.000      -0.176      -0.145
ar.L28        -0.1163      0.007    -16.401      0.000      -0.130      -0.102
ar.L29        -0.0781      0.006    -13.198      0.000      -0.090      -0.067
ar.L30        -0.0551      0.004    -13.570      0.000      -0.063      -0.047
ma.L1         -0.9999      0.002   -578.629      0.000      -1.003      -0.996
sigma2         0.0791      0.000    317.362      0.000       0.079       0.080
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):            531593.50
Prob(Q):                              0.98   Prob(JB):                         0.00
Heteroskedasticity (H):              27.21   Skew:                             1.03
Prob(H) (two-sided):                  0.00   Kurtosis:                        28.68
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223    43.584097
19224    43.630074
19225    43.666720
19226    43.676400
19227    43.685185
19228    43.743583
19229    43.778522
19230    43.855908
19231    43.935340
19232    43.966174
Name: predicted_mean, dtype: float64
       lower price  upper price
19223    43.032733    44.135461
19224    42.835469    44.424679
19225    42.681110    44.652329
19226    42.519536    44.833264
19227    42.373233    44.997137
19228    42.283745    45.203421
19229    42.181162    45.375883
19230    42.124200    45.587617
19231    42.070036    45.800643
19232    41.966258    45.966090

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

                            price short_name
timestamp                                   
2021-12-27 09:00:00+03:00   89.90      PGSUS
2021-12-27 10:00:00+03:00   91.65      PGSUS
2021-12-27 11:00:00+03:00   90.35      PGSUS
2021-12-27 12:00:00+03:00   90.15      PGSUS
2021-12-27 13:00:00+03:00   90.30      PGSUS
...                           ...        ...
2024-01-11 14:00:00+03:00  712.00      PGSUS
2024-01-11 15:00:00+03:00  708.00      PGSUS
2024-01-11 16:00:00+03:00  710.00      PGSUS
2024-01-11 17:00:00+03:00  712.00      PGSUS
2024-01-11 18:00:00+03:00  711.50      PGSUS

[5104 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    89.90
2021-12-27 07:00:00+00:00    91.65
2021-12-27 08:00:00+00:00    90.35
2021-12-27 09:00:00+00:00    90.15
2021-12-27 10:00:00+00:00    90.30
2021-12-27 11:00:00+00:00    88.95
2021-12-27 12:00:00+00:00    89.05
2021-12-27 13:00:00+00:00    88.90
2021-12-27 14:00:00+00:00    88.10
2021-12-27 15:00:00+00:00    87.90
2021-12-28 06:00:00+00:00    89.00
2021-12-28 07:00:00+00:00    88.70
2021-12-28 08:00:00+00:00    88.05
2021-12-28 09:00:00+00:00    88.05
2021-12-28 10:00:00+00:00    87.85
2021-12-28 11:00:00+00:00    87.30
2021-12-28 12:00:00+00:00    87.35
2021-12-28 13:00:00+00:00    86.05
2021-12-28 14:00:00+00:00    86.60
2021-12-28 15:00:00+00:00    86.30
2021-12-29 06:00:00+00:00    85.75
2021-12-29 07:00:00+00:00    84.50
2021-12-29 08:00:00+00:00    87.25
2021-12-29 09:00:00+00:00    87.25
2021-12-29 10:00:00+00:00    87.00
2021-12-29 11:00:00+00:00    87.35
2021-12-29 12:00:00+00:00    87.75
2021-12-29 13:00:00+00:00    89.15
2021-12-29 14:00:00+00:00    88.50
2021-12-29 15:00:00+00:00    88.20
2021-12-30 06:00:00+00:00    88.85
2021-12-30 07:00:00+00:00    87.85
2021-12-30 08:00:00+00:00    87.55
2021-12-30 09:00:00+00:00    86.95
2021-12-30 10:00:00+00:00    86.45
2021-12-30 11:00:00+00:00    85.00
2021-12-30 12:00:00+00:00    84.90
2021-12-30 13:00:00+00:00    85.75
2021-12-30 14:00:00+00:00    85.30
2021-12-30 15:00:00+00:00    85.15
2021-12-31 06:00:00+00:00    86.00
2021-12-31 07:00:00+00:00    87.10
2021-12-31 08:00:00+00:00    86.65
2021-12-31 09:00:00+00:00    87.70
2021-12-31 10:00:00+00:00    86.75
2021-12-31 11:00:00+00:00    86.90
2021-12-31 12:00:00+00:00    85.85
2021-12-31 13:00:00+00:00    85.65
2021-12-31 14:00:00+00:00    84.70
2021-12-31 15:00:00+00:00    84.95
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    702.0
2024-01-10 10:00:00+00:00    706.5
2024-01-10 10:30:00+00:00    704.5
2024-01-10 11:00:00+00:00    705.5
2024-01-10 11:30:00+00:00    708.0
2024-01-10 12:00:00+00:00    704.5
2024-01-10 12:30:00+00:00    711.5
2024-01-10 13:00:00+00:00    711.5
2024-01-10 13:30:00+00:00    712.0
2024-01-10 14:00:00+00:00    711.5
2024-01-10 15:00:00+00:00    711.0
2024-01-11 06:00:00+00:00    720.5
2024-01-11 06:30:00+00:00    718.0
2024-01-11 07:00:00+00:00    712.0
2024-01-11 07:30:00+00:00    713.5
2024-01-11 08:00:00+00:00    712.0
2024-01-11 08:30:00+00:00    713.5
2024-01-11 09:00:00+00:00    713.5
2024-01-11 09:30:00+00:00    713.0
2024-01-11 10:00:00+00:00    712.0
2024-01-11 10:30:00+00:00    711.5
2024-01-11 11:00:00+00:00    712.0
2024-01-11 11:30:00+00:00    711.5
2024-01-11 12:00:00+00:00    708.0
2024-01-11 12:30:00+00:00    713.5
2024-01-11 13:00:00+00:00    710.0
2024-01-11 13:30:00+00:00    710.0
2024-01-11 14:00:00+00:00    712.0
2024-01-11 14:30:00+00:00    711.5
2024-01-11 15:00:00+00:00    711.5
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    0.30
2018-01-02 11:00:00+03:00    0.74
2018-01-02 12:00:00+03:00    0.08
2018-01-02 13:00:00+03:00   -0.02
2018-01-02 14:00:00+03:00    0.20
                             ... 
2024-01-11 13:00:00+00:00   -3.50
2024-01-11 13:30:00+00:00    0.00
2024-01-11 14:00:00+00:00    2.00
2024-01-11 14:30:00+00:00   -0.50
2024-01-11 15:00:00+00:00    0.00
Name: price, Length: 19218, dtype: float64
ADF Statistic: -21.3666017673937
p-value: 0.0
Critical Values: {'1%': -3.430691131638002, '5%': -2.861690767836002, '10%': -2.5668502496617855}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19219
Model:                ARIMA(30, 3, 1)   Log Likelihood              -44135.418
Date:                Thu, 11 Jan 2024   AIC                          88334.837
Time:                        21:56:09   BIC                          88586.469
Sample:                             0   HQIC                         88417.322
                              - 19219                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.9753      0.002   -400.933      0.000      -0.980      -0.971
ar.L2         -0.9703      0.005   -212.180      0.000      -0.979      -0.961
ar.L3         -0.9504      0.006   -147.763      0.000      -0.963      -0.938
ar.L4         -0.9134      0.008   -119.701      0.000      -0.928      -0.898
ar.L5         -0.8768      0.009   -102.553      0.000      -0.894      -0.860
ar.L6         -0.8203      0.009    -91.343      0.000      -0.838      -0.803
ar.L7         -0.7919      0.010    -81.610      0.000      -0.811      -0.773
ar.L8         -0.7539      0.010    -72.484      0.000      -0.774      -0.734
ar.L9         -0.7200      0.011    -65.966      0.000      -0.741      -0.699
ar.L10        -0.6661      0.011    -58.713      0.000      -0.688      -0.644
ar.L11        -0.6271      0.012    -53.344      0.000      -0.650      -0.604
ar.L12        -0.6030      0.012    -49.885      0.000      -0.627      -0.579
ar.L13        -0.5520      0.012    -44.873      0.000      -0.576      -0.528
ar.L14        -0.5078      0.013    -40.310      0.000      -0.532      -0.483
ar.L15        -0.4942      0.013    -39.334      0.000      -0.519      -0.470
ar.L16        -0.4545      0.013    -36.206      0.000      -0.479      -0.430
ar.L17        -0.4059      0.013    -31.129      0.000      -0.432      -0.380
ar.L18        -0.3546      0.013    -27.797      0.000      -0.380      -0.330
ar.L19        -0.3111      0.013    -24.833      0.000      -0.336      -0.287
ar.L20        -0.3206      0.012    -26.649      0.000      -0.344      -0.297
ar.L21        -0.3276      0.012    -28.465      0.000      -0.350      -0.305
ar.L22        -0.3251      0.011    -29.751      0.000      -0.346      -0.304
ar.L23        -0.2822      0.010    -27.283      0.000      -0.303      -0.262
ar.L24        -0.2632      0.010    -26.064      0.000      -0.283      -0.243
ar.L25        -0.1944      0.010    -20.160      0.000      -0.213      -0.175
ar.L26        -0.1443      0.009    -16.528      0.000      -0.161      -0.127
ar.L27        -0.1088      0.008    -13.726      0.000      -0.124      -0.093
ar.L28        -0.0835      0.007    -11.923      0.000      -0.097      -0.070
ar.L29        -0.0657      0.005    -12.331      0.000      -0.076      -0.055
ar.L30        -0.0360      0.004    -10.109      0.000      -0.043      -0.029
ma.L1         -1.0000      0.007   -150.934      0.000      -1.013      -0.987
sigma2         5.7841      0.035    165.636      0.000       5.716       5.853
===================================================================================
Ljung-Box (L1) (Q):                   0.02   Jarque-Bera (JB):           3329222.85
Prob(Q):                              0.89   Prob(JB):                         0.00
Heteroskedasticity (H):              63.73   Skew:                             2.28
Prob(H) (two-sided):                  0.00   Kurtosis:                        67.32
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19219    711.687011
19220    711.536525
19221    711.644075
19222    711.850896
19223    712.533014
19224    712.680611
19225    712.859428
19226    713.403780
19227    713.231258
19228    713.066544
Name: predicted_mean, dtype: float64
       lower price  upper price
19219   706.973192   716.400829
19220   704.787079   718.285971
19221   703.328440   719.959711
19222   702.172147   721.529645
19223   701.577801   723.488228
19224   700.504333   724.856889
19225   699.459680   726.259176
19226   698.822363   727.985197
19227   697.481230   728.981286
19228   696.158721   729.974367

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

                           price short_name
timestamp                                  
2021-12-27 09:00:00+03:00   8.15      PETKM
2021-12-27 10:00:00+03:00   8.39      PETKM
2021-12-27 11:00:00+03:00   8.25      PETKM
2021-12-27 12:00:00+03:00   8.19      PETKM
2021-12-27 13:00:00+03:00   8.18      PETKM
...                          ...        ...
2024-01-11 14:00:00+03:00  20.24      PETKM
2024-01-11 15:00:00+03:00  20.06      PETKM
2024-01-11 16:00:00+03:00  20.10      PETKM
2024-01-11 17:00:00+03:00  20.02      PETKM
2024-01-11 18:00:00+03:00  20.04      PETKM

[5104 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    8.15
2021-12-27 07:00:00+00:00    8.39
2021-12-27 08:00:00+00:00    8.25
2021-12-27 09:00:00+00:00    8.19
2021-12-27 10:00:00+00:00    8.18
2021-12-27 11:00:00+00:00    8.12
2021-12-27 12:00:00+00:00    8.06
2021-12-27 13:00:00+00:00    8.06
2021-12-27 14:00:00+00:00    8.01
2021-12-27 15:00:00+00:00    8.01
2021-12-28 06:00:00+00:00    8.12
2021-12-28 07:00:00+00:00    8.06
2021-12-28 08:00:00+00:00    7.97
2021-12-28 09:00:00+00:00    7.93
2021-12-28 10:00:00+00:00    7.95
2021-12-28 11:00:00+00:00    7.88
2021-12-28 12:00:00+00:00    7.92
2021-12-28 13:00:00+00:00    7.90
2021-12-28 14:00:00+00:00    7.84
2021-12-28 15:00:00+00:00    7.82
2021-12-29 06:00:00+00:00    7.73
2021-12-29 07:00:00+00:00    7.77
2021-12-29 08:00:00+00:00    7.86
2021-12-29 09:00:00+00:00    7.88
2021-12-29 10:00:00+00:00    7.94
2021-12-29 11:00:00+00:00    7.95
2021-12-29 12:00:00+00:00    7.99
2021-12-29 13:00:00+00:00    8.03
2021-12-29 14:00:00+00:00    8.03
2021-12-29 15:00:00+00:00    8.05
2021-12-30 06:00:00+00:00    8.15
2021-12-30 07:00:00+00:00    8.10
2021-12-30 08:00:00+00:00    8.11
2021-12-30 09:00:00+00:00    8.03
2021-12-30 10:00:00+00:00    8.02
2021-12-30 11:00:00+00:00    7.89
2021-12-30 12:00:00+00:00    7.86
2021-12-30 13:00:00+00:00    7.90
2021-12-30 14:00:00+00:00    7.78
2021-12-30 15:00:00+00:00    7.74
2021-12-31 06:00:00+00:00    7.74
2021-12-31 07:00:00+00:00    7.82
2021-12-31 08:00:00+00:00    7.90
2021-12-31 09:00:00+00:00    7.91
2021-12-31 10:00:00+00:00    7.84
2021-12-31 11:00:00+00:00    7.86
2021-12-31 12:00:00+00:00    7.80
2021-12-31 13:00:00+00:00    7.81
2021-12-31 14:00:00+00:00    7.78
2021-12-31 15:00:00+00:00    7.80
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    19.930000
2024-01-10 10:00:00+00:00    19.920000
2024-01-10 10:30:00+00:00    19.900000
2024-01-10 11:00:00+00:00    19.880000
2024-01-10 11:30:00+00:00    19.809999
2024-01-10 12:00:00+00:00    19.730000
2024-01-10 12:30:00+00:00    19.860001
2024-01-10 13:00:00+00:00    20.000000
2024-01-10 13:30:00+00:00    20.080000
2024-01-10 14:00:00+00:00    20.220000
2024-01-10 15:00:00+00:00    20.100000
2024-01-11 06:00:00+00:00    20.240000
2024-01-11 06:30:00+00:00    20.260000
2024-01-11 07:00:00+00:00    20.460000
2024-01-11 07:30:00+00:00    20.379999
2024-01-11 08:00:00+00:00    20.300000
2024-01-11 08:30:00+00:00    20.379999
2024-01-11 09:00:00+00:00    20.380000
2024-01-11 09:30:00+00:00    20.280001
2024-01-11 10:00:00+00:00    20.320000
2024-01-11 10:30:00+00:00    20.299999
2024-01-11 11:00:00+00:00    20.240000
2024-01-11 11:30:00+00:00    20.139999
2024-01-11 12:00:00+00:00    20.060000
2024-01-11 12:30:00+00:00    20.299999
2024-01-11 13:00:00+00:00    20.100000
2024-01-11 13:30:00+00:00    20.059999
2024-01-11 14:00:00+00:00    20.020000
2024-01-11 14:30:00+00:00    20.040001
2024-01-11 15:00:00+00:00    20.040000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    3.380000e-02
2018-01-02 11:00:00+03:00    1.120000e-02
2018-01-02 12:00:00+03:00    5.600000e-03
2018-01-02 13:00:00+03:00    5.600000e-03
2018-01-02 14:00:00+03:00    2.820000e-02
                                 ...     
2024-01-11 13:00:00+00:00   -1.999992e-01
2024-01-11 13:30:00+00:00   -4.000053e-02
2024-01-11 14:00:00+00:00   -3.999947e-02
2024-01-11 14:30:00+00:00    2.000092e-02
2024-01-11 15:00:00+00:00   -9.155273e-07
Name: price, Length: 19223, dtype: float64
ADF Statistic: -21.65032493818614
p-value: 0.0
Critical Values: {'1%': -3.4306909537747425, '5%': -2.8616906892313936, '10%': -2.5668502078218833}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19224
Model:                ARIMA(30, 3, 1)   Log Likelihood               21432.767
Date:                Thu, 11 Jan 2024   AIC                         -42801.534
Time:                        21:59:13   BIC                         -42549.894
Sample:                             0   HQIC                        -42719.046
                              - 19224                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.0255      0.007   -154.658      0.000      -1.039      -1.013
ar.L2         -1.0497      0.012    -89.292      0.000      -1.073      -1.027
ar.L3         -1.0584      0.016    -64.587      0.000      -1.091      -1.026
ar.L4         -1.0711      0.020    -53.438      0.000      -1.110      -1.032
ar.L5         -1.0603      0.023    -45.387      0.000      -1.106      -1.014
ar.L6         -1.0418      0.026    -40.243      0.000      -1.093      -0.991
ar.L7         -1.0339      0.028    -37.034      0.000      -1.089      -0.979
ar.L8         -1.0208      0.029    -34.725      0.000      -1.078      -0.963
ar.L9         -1.0043      0.031    -32.586      0.000      -1.065      -0.944
ar.L10        -0.9765      0.032    -30.938      0.000      -1.038      -0.915
ar.L11        -0.9243      0.032    -28.751      0.000      -0.987      -0.861
ar.L12        -0.8980      0.032    -27.851      0.000      -0.961      -0.835
ar.L13        -0.8541      0.032    -26.483      0.000      -0.917      -0.791
ar.L14        -0.8254      0.032    -26.124      0.000      -0.887      -0.763
ar.L15        -0.7934      0.031    -25.525      0.000      -0.854      -0.732
ar.L16        -0.7439      0.030    -24.466      0.000      -0.804      -0.684
ar.L17        -0.6839      0.029    -23.337      0.000      -0.741      -0.626
ar.L18        -0.6172      0.028    -21.839      0.000      -0.673      -0.562
ar.L19        -0.5756      0.027    -21.391      0.000      -0.628      -0.523
ar.L20        -0.5494      0.025    -21.720      0.000      -0.599      -0.500
ar.L21        -0.5461      0.023    -23.348      0.000      -0.592      -0.500
ar.L22        -0.5150      0.022    -23.340      0.000      -0.558      -0.472
ar.L23        -0.4646      0.020    -22.795      0.000      -0.505      -0.425
ar.L24        -0.4160      0.019    -22.154      0.000      -0.453      -0.379
ar.L25        -0.3397      0.017    -20.356      0.000      -0.372      -0.307
ar.L26        -0.2837      0.014    -19.591      0.000      -0.312      -0.255
ar.L27        -0.2333      0.012    -18.846      0.000      -0.258      -0.209
ar.L28        -0.1834      0.010    -18.386      0.000      -0.203      -0.164
ar.L29        -0.1120      0.008    -14.715      0.000      -0.127      -0.097
ar.L30        -0.0497      0.005    -10.114      0.000      -0.059      -0.040
ma.L1         -0.8683      0.006   -142.675      0.000      -0.880      -0.856
sigma2         0.0062   2.01e-05    310.017      0.000       0.006       0.006
===================================================================================
Ljung-Box (L1) (Q):                   0.75   Jarque-Bera (JB):            411600.59
Prob(Q):                              0.39   Prob(JB):                         0.00
Heteroskedasticity (H):              25.29   Skew:                             0.67
Prob(H) (two-sided):                  0.00   Kurtosis:                        25.63
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224    20.016772
19225    19.994482
19226    19.973368
19227    19.947934
19228    19.927790
19229    19.916609
19230    19.906461
19231    19.901454
19232    19.886047
19233    19.857638
Name: predicted_mean, dtype: float64
       lower price  upper price
19224    19.861893    20.171650
19225    19.763531    20.225433
19226    19.675854    20.270882
19227    19.586182    20.309685
19228    19.502715    20.352866
19229    19.426449    20.406770
19230    19.348687    20.464235
19231    19.274251    20.528656
19232    19.187115    20.584980
19233    19.084403    20.630873

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00  12.4338      SAHOL
2021-12-27 10:00:00+03:00  12.5345      SAHOL
2021-12-27 11:00:00+03:00  12.4978      SAHOL
2021-12-27 12:00:00+03:00  12.4978      SAHOL
2021-12-27 13:00:00+03:00  12.5985      SAHOL
...                            ...        ...
2024-01-11 14:00:00+03:00  66.3000      SAHOL
2024-01-11 15:00:00+03:00  67.1500      SAHOL
2024-01-11 16:00:00+03:00  68.0000      SAHOL
2024-01-11 17:00:00+03:00  68.4500      SAHOL
2024-01-11 18:00:00+03:00  68.3500      SAHOL

[5104 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    12.4338
2021-12-27 07:00:00+00:00    12.5345
2021-12-27 08:00:00+00:00    12.4978
2021-12-27 09:00:00+00:00    12.4978
2021-12-27 10:00:00+00:00    12.5985
2021-12-27 11:00:00+00:00    12.4155
2021-12-27 12:00:00+00:00    12.3606
2021-12-27 13:00:00+00:00    12.3240
2021-12-27 14:00:00+00:00    12.2416
2021-12-27 15:00:00+00:00    12.2325
2021-12-28 06:00:00+00:00    12.3514
2021-12-28 07:00:00+00:00    12.3148
2021-12-28 08:00:00+00:00    12.2050
2021-12-28 09:00:00+00:00    12.1410
2021-12-28 10:00:00+00:00    12.0587
2021-12-28 11:00:00+00:00    11.9489
2021-12-28 12:00:00+00:00    12.0495
2021-12-28 13:00:00+00:00    11.9763
2021-12-28 14:00:00+00:00    11.9214
2021-12-28 15:00:00+00:00    11.9123
2021-12-29 06:00:00+00:00    11.8574
2021-12-29 07:00:00+00:00    11.7934
2021-12-29 08:00:00+00:00    11.9672
2021-12-29 09:00:00+00:00    12.0038
2021-12-29 10:00:00+00:00    12.0403
2021-12-29 11:00:00+00:00    12.0221
2021-12-29 12:00:00+00:00    12.1135
2021-12-29 13:00:00+00:00    12.2325
2021-12-29 14:00:00+00:00    12.1685
2021-12-29 15:00:00+00:00    12.2050
2021-12-30 06:00:00+00:00    12.2782
2021-12-30 07:00:00+00:00    12.1410
2021-12-30 08:00:00+00:00    12.2416
2021-12-30 09:00:00+00:00    12.1685
2021-12-30 10:00:00+00:00    12.2142
2021-12-30 11:00:00+00:00    12.0221
2021-12-30 12:00:00+00:00    12.0861
2021-12-30 13:00:00+00:00    12.1501
2021-12-30 14:00:00+00:00    12.0221
2021-12-30 15:00:00+00:00    11.9672
2021-12-31 06:00:00+00:00    11.9855
2021-12-31 07:00:00+00:00    12.0221
2021-12-31 08:00:00+00:00    12.0953
2021-12-31 09:00:00+00:00    12.1319
2021-12-31 10:00:00+00:00    12.0495
2021-12-31 11:00:00+00:00    12.0769
2021-12-31 12:00:00+00:00    12.0587
2021-12-31 13:00:00+00:00    12.0953
2021-12-31 14:00:00+00:00    12.0587
2021-12-31 15:00:00+00:00    12.1045
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    63.500000
2024-01-10 10:00:00+00:00    63.600000
2024-01-10 10:30:00+00:00    63.900002
2024-01-10 11:00:00+00:00    64.400000
2024-01-10 11:30:00+00:00    64.400002
2024-01-10 12:00:00+00:00    64.200000
2024-01-10 12:30:00+00:00    65.199997
2024-01-10 13:00:00+00:00    65.200000
2024-01-10 13:30:00+00:00    65.449997
2024-01-10 14:00:00+00:00    66.000000
2024-01-10 15:00:00+00:00    66.000000
2024-01-11 06:00:00+00:00    66.000000
2024-01-11 06:30:00+00:00    66.250000
2024-01-11 07:00:00+00:00    65.750000
2024-01-11 07:30:00+00:00    65.849998
2024-01-11 08:00:00+00:00    65.800000
2024-01-11 08:30:00+00:00    66.199997
2024-01-11 09:00:00+00:00    65.950000
2024-01-11 09:30:00+00:00    65.750000
2024-01-11 10:00:00+00:00    65.600000
2024-01-11 10:30:00+00:00    65.949997
2024-01-11 11:00:00+00:00    66.300000
2024-01-11 11:30:00+00:00    67.250000
2024-01-11 12:00:00+00:00    67.150000
2024-01-11 12:30:00+00:00    68.550003
2024-01-11 13:00:00+00:00    68.000000
2024-01-11 13:30:00+00:00    67.900002
2024-01-11 14:00:00+00:00    68.450000
2024-01-11 14:30:00+00:00    68.349998
2024-01-11 15:00:00+00:00    68.350000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    0.000000
2018-01-02 11:00:00+03:00    0.063000
2018-01-02 12:00:00+03:00   -0.031500
2018-01-02 13:00:00+03:00    0.023600
2018-01-02 14:00:00+03:00    0.015700
                               ...   
2024-01-11 13:00:00+00:00   -0.550003
2024-01-11 13:30:00+00:00   -0.099998
2024-01-11 14:00:00+00:00    0.549998
2024-01-11 14:30:00+00:00   -0.100002
2024-01-11 15:00:00+00:00    0.000002
Name: price, Length: 19222, dtype: float64
ADF Statistic: -22.133374861915275
p-value: 0.0
Critical Values: {'1%': -3.4306910248977847, '5%': -2.8616907206633995, '10%': -2.5668502245526077}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19223
Model:                ARIMA(30, 1, 1)   Log Likelihood               -2883.804
Date:                Thu, 11 Jan 2024   AIC                           5831.607
Time:                        22:02:01   BIC                           6083.249
Sample:                             0   HQIC                          5914.095
                              - 19223                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.3601      0.010   -142.008      0.000      -1.379      -1.341
ar.L2         -0.4891      0.008    -61.543      0.000      -0.505      -0.474
ar.L3         -0.1094      0.007    -15.244      0.000      -0.123      -0.095
ar.L4         -0.0305      0.008     -3.908      0.000      -0.046      -0.015
ar.L5          0.0195      0.008      2.459      0.014       0.004       0.035
ar.L6          0.0259      0.008      3.255      0.001       0.010       0.041
ar.L7          0.0304      0.008      3.771      0.000       0.015       0.046
ar.L8         -0.0078      0.008     -0.916      0.360      -0.024       0.009
ar.L9         -0.0505      0.009     -5.763      0.000      -0.068      -0.033
ar.L10         0.0153      0.009      1.709      0.087      -0.002       0.033
ar.L11         0.0333      0.009      3.900      0.000       0.017       0.050
ar.L12         0.0058      0.009      0.681      0.496      -0.011       0.023
ar.L13         0.0262      0.009      3.064      0.002       0.009       0.043
ar.L14        -0.0149      0.008     -1.775      0.076      -0.031       0.002
ar.L15        -0.0066      0.008     -0.811      0.417      -0.023       0.009
ar.L16        -0.1115      0.008    -14.747      0.000      -0.126      -0.097
ar.L17        -0.2205      0.007    -33.132      0.000      -0.234      -0.207
ar.L18         0.2221      0.006     34.853      0.000       0.210       0.235
ar.L19         0.5016      0.006     79.275      0.000       0.489       0.514
ar.L20        -0.0156      0.006     -2.401      0.016      -0.028      -0.003
ar.L21        -0.2478      0.007    -35.970      0.000      -0.261      -0.234
ar.L22        -0.1125      0.008    -14.177      0.000      -0.128      -0.097
ar.L23        -0.0852      0.008    -10.770      0.000      -0.101      -0.070
ar.L24        -0.0416      0.008     -5.160      0.000      -0.057      -0.026
ar.L25        -0.0179      0.008     -2.141      0.032      -0.034      -0.002
ar.L26         0.0404      0.009      4.644      0.000       0.023       0.057
ar.L27         0.0524      0.009      5.652      0.000       0.034       0.071
ar.L28        -0.0101      0.009     -1.080      0.280      -0.029       0.008
ar.L29         0.0069      0.009      0.794      0.427      -0.010       0.024
ar.L30         0.0873      0.006     14.595      0.000       0.076       0.099
ma.L1          0.7443      0.009     79.886      0.000       0.726       0.763
sigma2         0.0807      0.000    227.877      0.000       0.080       0.081
===================================================================================
Ljung-Box (L1) (Q):                  12.08   Jarque-Bera (JB):            146099.09
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):              52.53   Skew:                             0.02
Prob(H) (two-sided):                  0.00   Kurtosis:                        16.51
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223    68.384584
19224    68.217454
19225    68.181553
19226    68.291383
19227    68.472170
19228    68.476582
19229    68.276035
19230    68.222928
19231    68.248807
19232    68.404555
Name: predicted_mean, dtype: float64
       lower price  upper price
19223    67.827951    68.941218
19224    67.621156    68.813752
19225    67.459149    68.903956
19226    67.526687    69.056078
19227    67.613780    69.330560
19228    67.574424    69.378740
19229    67.296602    69.255467
19230    67.199561    69.246294
19231    67.165187    69.332427
19232    67.283371    69.525738

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00  10.5956       SASA
2021-12-27 10:00:00+03:00  11.0217       SASA
2021-12-27 11:00:00+03:00  11.0108       SASA
2021-12-27 12:00:00+03:00  11.0326       SASA
2021-12-27 13:00:00+03:00  11.0108       SASA
...                            ...        ...
2024-01-11 14:00:00+03:00  35.4600       SASA
2024-01-11 15:00:00+03:00  35.1400       SASA
2024-01-11 16:00:00+03:00  35.2000       SASA
2024-01-11 17:00:00+03:00  35.2400       SASA
2024-01-11 18:00:00+03:00  35.0800       SASA

[5102 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    10.5956
2021-12-27 07:00:00+00:00    11.0217
2021-12-27 08:00:00+00:00    11.0108
2021-12-27 09:00:00+00:00    11.0326
2021-12-27 10:00:00+00:00    11.0108
2021-12-27 11:00:00+00:00    10.9348
2021-12-27 12:00:00+00:00    10.8696
2021-12-27 13:00:00+00:00    11.0000
2021-12-27 14:00:00+00:00    11.1196
2021-12-27 15:00:00+00:00    11.1630
2021-12-28 06:00:00+00:00    11.3913
2021-12-28 07:00:00+00:00    11.4239
2021-12-28 08:00:00+00:00    11.2608
2021-12-28 09:00:00+00:00    11.2283
2021-12-28 10:00:00+00:00    11.1957
2021-12-28 11:00:00+00:00    11.1739
2021-12-28 12:00:00+00:00    11.1848
2021-12-28 13:00:00+00:00    11.2173
2021-12-28 14:00:00+00:00    11.4022
2021-12-28 15:00:00+00:00    11.4783
2021-12-29 06:00:00+00:00    11.5761
2021-12-29 07:00:00+00:00    11.4457
2021-12-29 08:00:00+00:00    11.5000
2021-12-29 09:00:00+00:00    11.5000
2021-12-29 10:00:00+00:00    11.4891
2021-12-29 11:00:00+00:00    11.5435
2021-12-29 12:00:00+00:00    11.4783
2021-12-29 13:00:00+00:00    11.4891
2021-12-29 14:00:00+00:00    11.6413
2021-12-29 15:00:00+00:00    11.5979
2021-12-30 06:00:00+00:00    11.7391
2021-12-30 07:00:00+00:00    11.7609
2021-12-30 08:00:00+00:00    11.7935
2021-12-30 09:00:00+00:00    11.7609
2021-12-30 10:00:00+00:00    11.8478
2021-12-30 11:00:00+00:00    11.9891
2021-12-30 12:00:00+00:00    11.9239
2021-12-30 13:00:00+00:00    12.0217
2021-12-30 14:00:00+00:00    11.8913
2021-12-30 15:00:00+00:00    11.8370
2021-12-31 06:00:00+00:00    11.7826
2021-12-31 07:00:00+00:00    11.6630
2021-12-31 08:00:00+00:00    11.7173
2021-12-31 09:00:00+00:00    11.7283
2021-12-31 10:00:00+00:00    11.6957
2021-12-31 11:00:00+00:00    11.6630
2021-12-31 12:00:00+00:00    11.6522
2021-12-31 13:00:00+00:00    11.6413
2021-12-31 14:00:00+00:00    11.5108
2021-12-31 15:00:00+00:00    11.4891
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    35.680000
2024-01-10 10:00:00+00:00    35.560000
2024-01-10 10:30:00+00:00    35.560001
2024-01-10 11:00:00+00:00    35.580000
2024-01-10 11:30:00+00:00    35.540001
2024-01-10 12:00:00+00:00    35.320000
2024-01-10 12:30:00+00:00    35.599998
2024-01-10 13:00:00+00:00    35.760000
2024-01-10 13:30:00+00:00    35.680000
2024-01-10 14:00:00+00:00    35.700000
2024-01-10 15:00:00+00:00    35.780000
2024-01-11 06:00:00+00:00    35.880000
2024-01-11 06:30:00+00:00    36.000000
2024-01-11 07:00:00+00:00    35.800000
2024-01-11 07:30:00+00:00    35.740002
2024-01-11 08:00:00+00:00    35.500000
2024-01-11 08:30:00+00:00    35.540001
2024-01-11 09:00:00+00:00    35.540000
2024-01-11 09:30:00+00:00    35.500000
2024-01-11 10:00:00+00:00    35.460000
2024-01-11 10:30:00+00:00    35.419998
2024-01-11 11:00:00+00:00    35.460000
2024-01-11 11:30:00+00:00    35.279999
2024-01-11 12:00:00+00:00    35.140000
2024-01-11 12:30:00+00:00    35.380001
2024-01-11 13:00:00+00:00    35.200000
2024-01-11 13:30:00+00:00    35.220001
2024-01-11 14:00:00+00:00    35.240000
2024-01-11 14:30:00+00:00    35.080002
2024-01-11 15:00:00+00:00    35.080000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    0.006900
2018-01-02 11:00:00+03:00    0.027700
2018-01-02 12:00:00+03:00    0.019300
2018-01-02 13:00:00+03:00    0.000000
2018-01-02 14:00:00+03:00    0.000000
                               ...   
2024-01-11 13:00:00+00:00   -0.180001
2024-01-11 13:30:00+00:00    0.020001
2024-01-11 14:00:00+00:00    0.019999
2024-01-11 14:30:00+00:00   -0.159998
2024-01-11 15:00:00+00:00   -0.000002
Name: price, Length: 19220, dtype: float64
ADF Statistic: -22.379868581384628
p-value: 0.0
Critical Values: {'1%': -3.4306910071142425, '5%': -2.8616907128041684, '10%': -2.5668502203692722}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19221
Model:                ARIMA(30, 1, 1)   Log Likelihood               -5300.323
Date:                Thu, 11 Jan 2024   AIC                          10664.646
Time:                        22:04:46   BIC                          10916.284
Sample:                             0   HQIC                         10747.133
                              - 19221                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.0894      0.004   -267.476      0.000      -1.097      -1.081
ar.L2         -0.1027      0.005    -21.763      0.000      -0.112      -0.093
ar.L3         -0.0254      0.005     -4.620      0.000      -0.036      -0.015
ar.L4         -0.0345      0.006     -6.151      0.000      -0.045      -0.023
ar.L5         -0.0167      0.005     -3.504      0.000      -0.026      -0.007
ar.L6         -0.0201      0.005     -4.198      0.000      -0.029      -0.011
ar.L7         -0.0073      0.005     -1.351      0.177      -0.018       0.003
ar.L8          0.0042      0.007      0.637      0.524      -0.009       0.017
ar.L9          0.0395      0.007      5.649      0.000       0.026       0.053
ar.L10         0.0430      0.007      6.465      0.000       0.030       0.056
ar.L11         0.0287      0.006      4.467      0.000       0.016       0.041
ar.L12        -0.0012      0.006     -0.198      0.843      -0.013       0.010
ar.L13         0.0127      0.005      2.353      0.019       0.002       0.023
ar.L14         0.0002      0.005      0.037      0.971      -0.010       0.011
ar.L15         0.0192      0.006      3.330      0.001       0.008       0.030
ar.L16         0.0481      0.006      8.560      0.000       0.037       0.059
ar.L17         0.0927      0.004     25.654      0.000       0.086       0.100
ar.L18         0.2211      0.002     91.579      0.000       0.216       0.226
ar.L19         0.1657      0.002     68.764      0.000       0.161       0.170
ar.L20        -0.0642      0.004    -17.711      0.000      -0.071      -0.057
ar.L21        -0.0452      0.005     -9.226      0.000      -0.055      -0.036
ar.L22        -0.0325      0.004     -7.328      0.000      -0.041      -0.024
ar.L23        -0.0661      0.005    -12.386      0.000      -0.077      -0.056
ar.L24        -0.0344      0.005     -6.394      0.000      -0.045      -0.024
ar.L25        -0.0532      0.005    -10.354      0.000      -0.063      -0.043
ar.L26         0.0011      0.006      0.172      0.864      -0.011       0.013
ar.L27        -0.0073      0.006     -1.309      0.191      -0.018       0.004
ar.L28        -0.0344      0.006     -5.444      0.000      -0.047      -0.022
ar.L29        -0.0198      0.006     -3.281      0.001      -0.032      -0.008
ar.L30         0.0280      0.004      7.746      0.000       0.021       0.035
ma.L1          0.9125      0.003    295.646      0.000       0.906       0.919
sigma2         0.1016      0.000    490.524      0.000       0.101       0.102
===================================================================================
Ljung-Box (L1) (Q):                   0.02   Jarque-Bera (JB):          14561490.06
Prob(Q):                              0.88   Prob(JB):                         0.00
Heteroskedasticity (H):            1631.44   Skew:                            -0.86
Prob(H) (two-sided):                  0.00   Kurtosis:                       137.83
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19221    35.072892
19222    35.007492
19223    34.988249
19224    34.964652
19225    34.954591
19226    34.962518
19227    34.958805
19228    34.957794
19229    34.952837
19230    34.945019
Name: predicted_mean, dtype: float64
       lower price  upper price
19221    34.448093    35.697690
19222    34.198263    35.816721
19223    33.998117    35.978380
19224    33.853288    36.076016
19225    33.713733    36.195449
19226    33.624987    36.300048
19227    33.516081    36.401529
19228    33.431299    36.484289
19229    33.332503    36.573170
19230    33.242014    36.648023
                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00  13.3510       SISE
2021-12-27 10:00:00+03:00  13.5826       SISE
2021-12-27 11:00:00+03:00  13.4379       SISE
2021-12-27 12:00:00+03:00  13.4282       SISE
2021-12-27 13:00:00+03:00  13.4186       SISE
...                            ...        ...
2024-01-11 14:00:00+03:00  48.1400       SISE
2024-01-11 15:00:00+03:00  47.9200       SISE
2024-01-11 16:00:00+03:00  47.8600       SISE
2024-01-11 17:00:00+03:00  47.8600       SISE
2024-01-11 18:00:00+03:00  47.8000       SISE

[5104 rows x 2 columns]

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    13.3510
2021-12-27 07:00:00+00:00    13.5826
2021-12-27 08:00:00+00:00    13.4379
2021-12-27 09:00:00+00:00    13.4282
2021-12-27 10:00:00+00:00    13.4186
2021-12-27 11:00:00+00:00    13.3028
2021-12-27 12:00:00+00:00    13.1870
2021-12-27 13:00:00+00:00    13.2352
2021-12-27 14:00:00+00:00    13.1099
2021-12-27 15:00:00+00:00    13.0712
2021-12-28 06:00:00+00:00    13.3221
2021-12-28 07:00:00+00:00    13.4861
2021-12-28 08:00:00+00:00    13.4282
2021-12-28 09:00:00+00:00    13.3896
2021-12-28 10:00:00+00:00    13.3606
2021-12-28 11:00:00+00:00    13.1870
2021-12-28 12:00:00+00:00    13.2739
2021-12-28 13:00:00+00:00    13.1581
2021-12-28 14:00:00+00:00    12.9941
2021-12-28 15:00:00+00:00    12.9363
2021-12-29 06:00:00+00:00    12.9265
2021-12-29 07:00:00+00:00    12.9169
2021-12-29 08:00:00+00:00    13.1677
2021-12-29 09:00:00+00:00    13.2256
2021-12-29 10:00:00+00:00    13.2063
2021-12-29 11:00:00+00:00    13.2063
2021-12-29 12:00:00+00:00    13.3124
2021-12-29 13:00:00+00:00    13.3703
2021-12-29 14:00:00+00:00    13.3703
2021-12-29 15:00:00+00:00    13.3606
2021-12-30 06:00:00+00:00    13.6019
2021-12-30 07:00:00+00:00    13.3993
2021-12-30 08:00:00+00:00    13.4186
2021-12-30 09:00:00+00:00    13.3124
2021-12-30 10:00:00+00:00    13.3414
2021-12-30 11:00:00+00:00    13.1485
2021-12-30 12:00:00+00:00    13.1099
2021-12-30 13:00:00+00:00    13.1774
2021-12-30 14:00:00+00:00    13.0134
2021-12-30 15:00:00+00:00    12.9941
2021-12-31 06:00:00+00:00    12.9941
2021-12-31 07:00:00+00:00    13.0712
2021-12-31 08:00:00+00:00    13.2642
2021-12-31 09:00:00+00:00    13.3221
2021-12-31 10:00:00+00:00    13.0712
2021-12-31 11:00:00+00:00    13.1388
2021-12-31 12:00:00+00:00    13.0906
2021-12-31 13:00:00+00:00    13.0230
2021-12-31 14:00:00+00:00    12.9265
2021-12-31 15:00:00+00:00    12.9072
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    47.599998
2024-01-10 10:00:00+00:00    47.700000
2024-01-10 10:30:00+00:00    47.720001
2024-01-10 11:00:00+00:00    47.700000
2024-01-10 11:30:00+00:00    47.720001
2024-01-10 12:00:00+00:00    47.480000
2024-01-10 12:30:00+00:00    47.840000
2024-01-10 13:00:00+00:00    48.040000
2024-01-10 13:30:00+00:00    48.180000
2024-01-10 14:00:00+00:00    48.420000
2024-01-10 15:00:00+00:00    48.460000
2024-01-11 06:00:00+00:00    48.460000
2024-01-11 06:30:00+00:00    48.459999
2024-01-11 07:00:00+00:00    48.320000
2024-01-11 07:30:00+00:00    48.220001
2024-01-11 08:00:00+00:00    48.080000
2024-01-11 08:30:00+00:00    48.320000
2024-01-11 09:00:00+00:00    48.340000
2024-01-11 09:30:00+00:00    48.299999
2024-01-11 10:00:00+00:00    48.240000
2024-01-11 10:30:00+00:00    48.200001
2024-01-11 11:00:00+00:00    48.140000
2024-01-11 11:30:00+00:00    48.080002
2024-01-11 12:00:00+00:00    47.920000
2024-01-11 12:30:00+00:00    48.220001
2024-01-11 13:00:00+00:00    47.860000
2024-01-11 13:30:00+00:00    47.820000
2024-01-11 14:00:00+00:00    47.860000
2024-01-11 14:30:00+00:00    47.799999
2024-01-11 15:00:00+00:00    47.800000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00   -5.130000e-02
2018-01-02 11:00:00+03:00    2.560000e-02
2018-01-02 12:00:00+03:00    0.000000e+00
2018-01-02 13:00:00+03:00    0.000000e+00
2018-01-02 14:00:00+03:00    2.570000e-02
                                 ...     
2024-01-11 13:00:00+00:00   -3.600012e-01
2024-01-11 13:30:00+00:00   -4.000031e-02
2024-01-11 14:00:00+00:00    4.000031e-02
2024-01-11 14:30:00+00:00   -6.000076e-02
2024-01-11 15:00:00+00:00    7.629395e-07
Name: price, Length: 19222, dtype: float64
ADF Statistic: -22.219929304142656
p-value: 0.0
Critical Values: {'1%': -3.4306910604704344, '5%': -2.86169073638432, '10%': -2.566850232920588}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19223
Model:                ARIMA(30, 1, 1)   Log Likelihood                3630.416
Date:                Thu, 11 Jan 2024   AIC                          -7196.832
Time:                        22:07:40   BIC                          -6945.190
Sample:                             0   HQIC                         -7114.344
                              - 19223                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.1790      0.005   -243.250      0.000      -1.189      -1.170
ar.L2         -0.1649      0.004    -37.298      0.000      -0.174      -0.156
ar.L3          0.0057      0.006      1.014      0.311      -0.005       0.017
ar.L4         -0.0372      0.006     -5.955      0.000      -0.049      -0.025
ar.L5          0.0027      0.006      0.459      0.646      -0.009       0.014
ar.L6         -0.0157      0.006     -2.624      0.009      -0.027      -0.004
ar.L7         -0.0218      0.006     -3.648      0.000      -0.034      -0.010
ar.L8         -0.0186      0.006     -2.962      0.003      -0.031      -0.006
ar.L9          0.0083      0.006      1.317      0.188      -0.004       0.021
ar.L10         0.0554      0.007      8.025      0.000       0.042       0.069
ar.L11         0.0579      0.007      8.439      0.000       0.044       0.071
ar.L12         0.0259      0.007      3.711      0.000       0.012       0.040
ar.L13         0.0323      0.007      4.736      0.000       0.019       0.046
ar.L14         0.0178      0.007      2.693      0.007       0.005       0.031
ar.L15         0.0077      0.007      1.158      0.247      -0.005       0.021
ar.L16        -0.0439      0.006     -7.028      0.000      -0.056      -0.032
ar.L17        -0.0441      0.006     -7.670      0.000      -0.055      -0.033
ar.L18         0.0896      0.005     17.477      0.000       0.080       0.100
ar.L19         0.1744      0.005     38.572      0.000       0.166       0.183
ar.L20        -0.0715      0.005    -13.340      0.000      -0.082      -0.061
ar.L21        -0.1753      0.006    -31.174      0.000      -0.186      -0.164
ar.L22        -0.0612      0.007     -8.841      0.000      -0.075      -0.048
ar.L23        -0.0516      0.006     -8.039      0.000      -0.064      -0.039
ar.L24        -0.0112      0.006     -1.800      0.072      -0.023       0.001
ar.L25         0.0145      0.006      2.364      0.018       0.002       0.027
ar.L26         0.0308      0.007      4.638      0.000       0.018       0.044
ar.L27         0.0009      0.007      0.127      0.899      -0.013       0.015
ar.L28        -0.0349      0.008     -4.594      0.000      -0.050      -0.020
ar.L29        -0.0151      0.007     -2.109      0.035      -0.029      -0.001
ar.L30         0.0527      0.005     10.223      0.000       0.043       0.063
ma.L1          0.8728      0.004    220.299      0.000       0.865       0.881
sigma2         0.0401      0.000    289.728      0.000       0.040       0.040
===================================================================================
Ljung-Box (L1) (Q):                   0.24   Jarque-Bera (JB):            530239.91
Prob(Q):                              0.63   Prob(JB):                         0.00
Heteroskedasticity (H):              65.26   Skew:                             0.79
Prob(H) (two-sided):                  0.00   Kurtosis:                        28.68
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223    47.752453
19224    47.772268
19225    47.710635
19226    47.751871
19227    47.745092
19228    47.802465
19229    47.756287
19230    47.760609
19231    47.740262
19232    47.756066
Name: predicted_mean, dtype: float64
       lower price  upper price
19223    47.359777    48.145129
19224    47.294345    48.250192
19225    47.118574    48.302696
19226    47.096645    48.407097
19227    47.009750    48.480435
19228    47.012552    48.592378
19229    46.902748    48.609825
19230    46.860389    48.660828
19231    46.786207    48.694317
19232    46.757779    48.754354

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

                            price short_name
timestamp                                   
2021-12-27 09:00:00+03:00   33.24      TAVHL
2021-12-27 10:00:00+03:00   33.68      TAVHL
2021-12-27 11:00:00+03:00   33.36      TAVHL
2021-12-27 12:00:00+03:00   33.20      TAVHL
2021-12-27 13:00:00+03:00   33.20      TAVHL
...                           ...        ...
2024-01-11 14:00:00+03:00  117.30      TAVHL
2024-01-11 15:00:00+03:00  116.30      TAVHL
2024-01-11 16:00:00+03:00  117.00      TAVHL
2024-01-11 17:00:00+03:00  117.80      TAVHL
2024-01-11 18:00:00+03:00  117.80      TAVHL

[5104 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    33.24
2021-12-27 07:00:00+00:00    33.68
2021-12-27 08:00:00+00:00    33.36
2021-12-27 09:00:00+00:00    33.20
2021-12-27 10:00:00+00:00    33.20
2021-12-27 11:00:00+00:00    32.64
2021-12-27 12:00:00+00:00    32.76
2021-12-27 13:00:00+00:00    32.78
2021-12-27 14:00:00+00:00    32.60
2021-12-27 15:00:00+00:00    32.50
2021-12-28 06:00:00+00:00    32.60
2021-12-28 07:00:00+00:00    32.90
2021-12-28 08:00:00+00:00    32.70
2021-12-28 09:00:00+00:00    32.56
2021-12-28 10:00:00+00:00    32.42
2021-12-28 11:00:00+00:00    32.18
2021-12-28 12:00:00+00:00    32.30
2021-12-28 13:00:00+00:00    31.50
2021-12-28 14:00:00+00:00    31.54
2021-12-28 15:00:00+00:00    31.56
2021-12-29 06:00:00+00:00    31.12
2021-12-29 07:00:00+00:00    31.22
2021-12-29 08:00:00+00:00    31.56
2021-12-29 09:00:00+00:00    31.78
2021-12-29 10:00:00+00:00    31.78
2021-12-29 11:00:00+00:00    32.14
2021-12-29 12:00:00+00:00    32.50
2021-12-29 13:00:00+00:00    32.74
2021-12-29 14:00:00+00:00    32.38
2021-12-29 15:00:00+00:00    32.50
2021-12-30 06:00:00+00:00    32.98
2021-12-30 07:00:00+00:00    32.10
2021-12-30 08:00:00+00:00    32.10
2021-12-30 09:00:00+00:00    31.92
2021-12-30 10:00:00+00:00    31.80
2021-12-30 11:00:00+00:00    31.34
2021-12-30 12:00:00+00:00    31.48
2021-12-30 13:00:00+00:00    31.48
2021-12-30 14:00:00+00:00    31.16
2021-12-30 15:00:00+00:00    31.08
2021-12-31 06:00:00+00:00    31.58
2021-12-31 07:00:00+00:00    32.44
2021-12-31 08:00:00+00:00    32.36
2021-12-31 09:00:00+00:00    33.00
2021-12-31 10:00:00+00:00    32.50
2021-12-31 11:00:00+00:00    32.60
2021-12-31 12:00:00+00:00    32.26
2021-12-31 13:00:00+00:00    32.18
2021-12-31 14:00:00+00:00    31.96
2021-12-31 15:00:00+00:00    32.70
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    117.400002
2024-01-10 10:00:00+00:00    117.700000
2024-01-10 10:30:00+00:00    117.400002
2024-01-10 11:00:00+00:00    117.500000
2024-01-10 11:30:00+00:00    117.300003
2024-01-10 12:00:00+00:00    116.900000
2024-01-10 12:30:00+00:00    117.099998
2024-01-10 13:00:00+00:00    117.800000
2024-01-10 13:30:00+00:00    117.500000
2024-01-10 14:00:00+00:00    117.400000
2024-01-10 15:00:00+00:00    117.000000
2024-01-11 06:00:00+00:00    117.500000
2024-01-11 06:30:00+00:00    118.300003
2024-01-11 07:00:00+00:00    116.800000
2024-01-11 07:30:00+00:00    117.400002
2024-01-11 08:00:00+00:00    117.000000
2024-01-11 08:30:00+00:00    117.199997
2024-01-11 09:00:00+00:00    117.100000
2024-01-11 09:30:00+00:00    117.000000
2024-01-11 10:00:00+00:00    117.000000
2024-01-11 10:30:00+00:00    116.800003
2024-01-11 11:00:00+00:00    117.300000
2024-01-11 11:30:00+00:00    117.000000
2024-01-11 12:00:00+00:00    116.300000
2024-01-11 12:30:00+00:00    117.400002
2024-01-11 13:00:00+00:00    117.000000
2024-01-11 13:30:00+00:00    116.900002
2024-01-11 14:00:00+00:00    117.800000
2024-01-11 14:30:00+00:00    117.800003
2024-01-11 15:00:00+00:00    117.800000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    0.163500
2018-01-02 11:00:00+03:00   -0.065400
2018-01-02 12:00:00+03:00    0.049000
2018-01-02 13:00:00+03:00    0.000000
2018-01-02 14:00:00+03:00   -0.016400
                               ...   
2024-01-11 13:00:00+00:00   -0.400002
2024-01-11 13:30:00+00:00   -0.099998
2024-01-11 14:00:00+00:00    0.899998
2024-01-11 14:30:00+00:00    0.000003
2024-01-11 15:00:00+00:00   -0.000003
Name: price, Length: 19222, dtype: float64
ADF Statistic: -22.076741212277536
p-value: 0.0
Critical Values: {'1%': -3.4306910426831823, '5%': -2.86169072852345, '10%': -2.5668502287363797}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19223
Model:                ARIMA(30, 3, 1)   Log Likelihood              -10657.133
Date:                Thu, 11 Jan 2024   AIC                          21378.265
Time:                        22:11:40   BIC                          21629.904
Sample:                             0   HQIC                         21460.752
                              - 19223                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.9974      0.003   -320.530      0.000      -1.004      -0.991
ar.L2         -0.9792      0.005   -202.874      0.000      -0.989      -0.970
ar.L3         -0.9442      0.007   -139.432      0.000      -0.957      -0.931
ar.L4         -0.9051      0.008   -115.448      0.000      -0.921      -0.890
ar.L5         -0.8718      0.009    -98.000      0.000      -0.889      -0.854
ar.L6         -0.8218      0.010    -82.963      0.000      -0.841      -0.802
ar.L7         -0.7926      0.011    -74.090      0.000      -0.814      -0.772
ar.L8         -0.7526      0.012    -64.364      0.000      -0.776      -0.730
ar.L9         -0.7206      0.012    -58.965      0.000      -0.745      -0.697
ar.L10        -0.6667      0.013    -52.090      0.000      -0.692      -0.642
ar.L11        -0.6182      0.014    -45.760      0.000      -0.645      -0.592
ar.L12        -0.6034      0.014    -43.542      0.000      -0.631      -0.576
ar.L13        -0.5682      0.014    -40.159      0.000      -0.596      -0.540
ar.L14        -0.5187      0.014    -36.369      0.000      -0.547      -0.491
ar.L15        -0.4934      0.015    -34.015      0.000      -0.522      -0.465
ar.L16        -0.4566      0.014    -31.635      0.000      -0.485      -0.428
ar.L17        -0.4141      0.014    -29.519      0.000      -0.442      -0.387
ar.L18        -0.3836      0.014    -27.244      0.000      -0.411      -0.356
ar.L19        -0.3370      0.014    -24.073      0.000      -0.364      -0.310
ar.L20        -0.3408      0.014    -24.640      0.000      -0.368      -0.314
ar.L21        -0.3360      0.014    -24.615      0.000      -0.363      -0.309
ar.L22        -0.3025      0.013    -23.594      0.000      -0.328      -0.277
ar.L23        -0.2842      0.012    -23.042      0.000      -0.308      -0.260
ar.L24        -0.2524      0.012    -21.074      0.000      -0.276      -0.229
ar.L25        -0.2077      0.011    -18.202      0.000      -0.230      -0.185
ar.L26        -0.1672      0.011    -15.902      0.000      -0.188      -0.147
ar.L27        -0.1584      0.010    -16.652      0.000      -0.177      -0.140
ar.L28        -0.1141      0.008    -14.024      0.000      -0.130      -0.098
ar.L29        -0.0691      0.007    -10.035      0.000      -0.083      -0.056
ar.L30        -0.0444      0.005     -9.122      0.000      -0.054      -0.035
ma.L1         -1.0000      0.008   -126.569      0.000      -1.015      -0.984
sigma2         0.1773      0.001    127.644      0.000       0.175       0.180
===================================================================================
Ljung-Box (L1) (Q):                   0.07   Jarque-Bera (JB):            443976.76
Prob(Q):                              0.80   Prob(JB):                         0.00
Heteroskedasticity (H):              11.03   Skew:                             0.48
Prob(H) (two-sided):                  0.00   Kurtosis:                        26.53
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223    117.848886
19224    117.884004
19225    117.868083
19226    117.892985
19227    117.907172
19228    117.956668
19229    117.965660
19230    118.022084
19231    118.086217
19232    118.027670
Name: predicted_mean, dtype: float64
       lower price  upper price
19223   117.023576   118.674196
19224   116.715305   119.052703
19225   116.427303   119.308862
19226   116.209098   119.576872
19227   115.995836   119.818508
19228   115.829748   120.083589
19229   115.625204   120.306117
19230   115.475209   120.568959
19231   115.334291   120.838143
19232   115.073156   120.982185

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00  19.0042      TKFEN
2021-12-27 10:00:00+03:00  19.1636      TKFEN
2021-12-27 11:00:00+03:00  18.8271      TKFEN
2021-12-27 12:00:00+03:00  18.7563      TKFEN
2021-12-27 13:00:00+03:00  18.7563      TKFEN
...                            ...        ...
2024-01-11 14:00:00+03:00  37.5600      TKFEN
2024-01-11 15:00:00+03:00  37.1800      TKFEN
2024-01-11 16:00:00+03:00  37.4600      TKFEN
2024-01-11 17:00:00+03:00  37.5000      TKFEN
2024-01-11 18:00:00+03:00  37.4200      TKFEN

[5104 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    19.0042
2021-12-27 07:00:00+00:00    19.1636
2021-12-27 08:00:00+00:00    18.8271
2021-12-27 09:00:00+00:00    18.7563
2021-12-27 10:00:00+00:00    18.7563
2021-12-27 11:00:00+00:00    18.5791
2021-12-27 12:00:00+00:00    18.6146
2021-12-27 13:00:00+00:00    18.5791
2021-12-27 14:00:00+00:00    18.5082
2021-12-27 15:00:00+00:00    18.4374
2021-12-28 06:00:00+00:00    18.5968
2021-12-28 07:00:00+00:00    18.4906
2021-12-28 08:00:00+00:00    18.4020
2021-12-28 09:00:00+00:00    18.2781
2021-12-28 10:00:00+00:00    18.0478
2021-12-28 11:00:00+00:00    17.9592
2021-12-28 12:00:00+00:00    18.1364
2021-12-28 13:00:00+00:00    17.9060
2021-12-28 14:00:00+00:00    17.7113
2021-12-28 15:00:00+00:00    17.6581
2021-12-29 06:00:00+00:00    17.5696
2021-12-29 07:00:00+00:00    17.4987
2021-12-29 08:00:00+00:00    17.9415
2021-12-29 09:00:00+00:00    17.9769
2021-12-29 10:00:00+00:00    18.1718
2021-12-29 11:00:00+00:00    18.1009
2021-12-29 12:00:00+00:00    18.1895
2021-12-29 13:00:00+00:00    18.3666
2021-12-29 14:00:00+00:00    18.1895
2021-12-29 15:00:00+00:00    18.1895
2021-12-30 06:00:00+00:00    18.5614
2021-12-30 07:00:00+00:00    18.1186
2021-12-30 08:00:00+00:00    18.1718
2021-12-30 09:00:00+00:00    18.1009
2021-12-30 10:00:00+00:00    18.2249
2021-12-30 11:00:00+00:00    17.8884
2021-12-30 12:00:00+00:00    17.8530
2021-12-30 13:00:00+00:00    17.9060
2021-12-30 14:00:00+00:00    17.7998
2021-12-30 15:00:00+00:00    17.7820
2021-12-31 06:00:00+00:00    17.7998
2021-12-31 07:00:00+00:00    17.7291
2021-12-31 08:00:00+00:00    17.8706
2021-12-31 09:00:00+00:00    18.0832
2021-12-31 10:00:00+00:00    17.9415
2021-12-31 11:00:00+00:00    18.0832
2021-12-31 12:00:00+00:00    17.9769
2021-12-31 13:00:00+00:00    17.9769
2021-12-31 14:00:00+00:00    17.7820
2021-12-31 15:00:00+00:00    17.9238
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    36.860001
2024-01-10 10:00:00+00:00    36.900000
2024-01-10 10:30:00+00:00    36.880001
2024-01-10 11:00:00+00:00    37.020000
2024-01-10 11:30:00+00:00    36.980000
2024-01-10 12:00:00+00:00    36.620000
2024-01-10 12:30:00+00:00    36.939999
2024-01-10 13:00:00+00:00    37.140000
2024-01-10 13:30:00+00:00    37.259998
2024-01-10 14:00:00+00:00    37.260000
2024-01-10 15:00:00+00:00    37.100000
2024-01-11 06:00:00+00:00    37.060000
2024-01-11 06:30:00+00:00    37.560001
2024-01-11 07:00:00+00:00    37.580000
2024-01-11 07:30:00+00:00    37.619999
2024-01-11 08:00:00+00:00    37.560000
2024-01-11 08:30:00+00:00    37.639999
2024-01-11 09:00:00+00:00    37.620000
2024-01-11 09:30:00+00:00    37.639999
2024-01-11 10:00:00+00:00    37.520000
2024-01-11 10:30:00+00:00    37.360001
2024-01-11 11:00:00+00:00    37.560000
2024-01-11 11:30:00+00:00    37.419998
2024-01-11 12:00:00+00:00    37.180000
2024-01-11 12:30:00+00:00    37.560001
2024-01-11 13:00:00+00:00    37.460000
2024-01-11 13:30:00+00:00    37.400002
2024-01-11 14:00:00+00:00    37.500000
2024-01-11 14:30:00+00:00    37.419998
2024-01-11 15:00:00+00:00    37.420000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    0.060000
2018-01-02 11:00:00+03:00   -0.097400
2018-01-02 12:00:00+03:00   -0.112300
2018-01-02 13:00:00+03:00    0.000000
2018-01-02 14:00:00+03:00    0.007500
                               ...   
2024-01-11 13:00:00+00:00   -0.100001
2024-01-11 13:30:00+00:00   -0.059998
2024-01-11 14:00:00+00:00    0.099998
2024-01-11 14:30:00+00:00   -0.080002
2024-01-11 15:00:00+00:00    0.000002
Name: price, Length: 19223, dtype: float64
ADF Statistic: -21.454468857642006
p-value: 0.0
Critical Values: {'1%': -3.4306909715527216, '5%': -2.8616906970881657, '10%': -2.5668502120039105}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19224
Model:                ARIMA(30, 1, 1)   Log Likelihood              -11958.641
Date:                Thu, 11 Jan 2024   AIC                          23981.283
Time:                        22:14:18   BIC                          24232.926
Sample:                             0   HQIC                         24063.771
                              - 19224                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.7061      0.147     -4.794      0.000      -0.995      -0.417
ar.L2          0.0396      0.130      0.304      0.761      -0.216       0.295
ar.L3         -0.0227      0.018     -1.247      0.212      -0.058       0.013
ar.L4          0.0173      0.009      1.945      0.052      -0.000       0.035
ar.L5         -0.0389      0.007     -5.684      0.000      -0.052      -0.025
ar.L6          0.0143      0.009      1.603      0.109      -0.003       0.032
ar.L7         -0.0337      0.007     -4.499      0.000      -0.048      -0.019
ar.L8          0.0092      0.009      1.007      0.314      -0.009       0.027
ar.L9         -0.0162      0.008     -2.143      0.032      -0.031      -0.001
ar.L10         0.0461      0.008      5.628      0.000       0.030       0.062
ar.L11        -0.0042      0.009     -0.464      0.643      -0.022       0.013
ar.L12        -0.0310      0.008     -4.060      0.000      -0.046      -0.016
ar.L13        -0.0159      0.007     -2.244      0.025      -0.030      -0.002
ar.L14        -0.0464      0.007     -6.931      0.000      -0.060      -0.033
ar.L15        -0.0301      0.010     -3.165      0.002      -0.049      -0.011
ar.L16        -0.1793      0.007    -24.472      0.000      -0.194      -0.165
ar.L17        -0.0726      0.028     -2.592      0.010      -0.128      -0.018
ar.L18         0.3952      0.016     24.388      0.000       0.363       0.427
ar.L19         0.4423      0.055      7.974      0.000       0.334       0.551
ar.L20        -0.1702      0.075     -2.266      0.023      -0.317      -0.023
ar.L21        -0.1786      0.013    -13.855      0.000      -0.204      -0.153
ar.L22        -0.0410      0.029     -1.411      0.158      -0.098       0.016
ar.L23        -0.0661      0.013     -5.238      0.000      -0.091      -0.041
ar.L24         0.0061      0.013      0.453      0.650      -0.020       0.032
ar.L25        -0.0298      0.007     -4.019      0.000      -0.044      -0.015
ar.L26         0.0310      0.009      3.488      0.000       0.014       0.048
ar.L27        -0.0237      0.009     -2.651      0.008      -0.041      -0.006
ar.L28         0.0018      0.008      0.216      0.829      -0.015       0.018
ar.L29        -0.0414      0.008     -5.297      0.000      -0.057      -0.026
ar.L30         0.0550      0.012      4.747      0.000       0.032       0.078
ma.L1         -0.1771      0.147     -1.203      0.229      -0.466       0.111
sigma2         0.2038      0.001    210.935      0.000       0.202       0.206
===================================================================================
Ljung-Box (L1) (Q):                   0.04   Jarque-Bera (JB):            122009.29
Prob(Q):                              0.84   Prob(JB):                         0.00
Heteroskedasticity (H):              12.65   Skew:                            -0.93
Prob(H) (two-sided):                  0.00   Kurtosis:                        15.20
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224    37.574019
19225    37.717914
19226    37.575807
19227    37.583684
19228    37.585210
19229    37.573491
19230    37.602978
19231    37.522680
19232    37.481545
19233    37.569202
Name: predicted_mean, dtype: float64
       lower price  upper price
19224    36.689218    38.458821
19225    36.827098    38.608730
19226    36.448923    38.702690
19227    36.434597    38.732771
19228    36.284404    38.886016
19229    36.247304    38.899678
19230    36.153351    39.052605
19231    36.049939    38.995422
19232    35.899361    39.063728
19233    35.965116    39.173288

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

                              price short_name
timestamp                                     
2021-12-27 09:00:00+03:00   19.6507      TUPRS
2021-12-27 10:00:00+03:00   20.4990      TUPRS
2021-12-27 11:00:00+03:00   20.2331      TUPRS
2021-12-27 12:00:00+03:00   19.9926      TUPRS
2021-12-27 13:00:00+03:00   20.0052      TUPRS
...                             ...        ...
2024-01-11 14:00:00+03:00  140.8000      TUPRS
2024-01-11 15:00:00+03:00  139.8000      TUPRS
2024-01-11 16:00:00+03:00  140.2000      TUPRS
2024-01-11 17:00:00+03:00  140.0000      TUPRS
2024-01-11 18:00:00+03:00  139.6000      TUPRS

[5104 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    19.6507
2021-12-27 07:00:00+00:00    20.4990
2021-12-27 08:00:00+00:00    20.2331
2021-12-27 09:00:00+00:00    19.9926
2021-12-27 10:00:00+00:00    20.0052
2021-12-27 11:00:00+00:00    19.7140
2021-12-27 12:00:00+00:00    19.6887
2021-12-27 13:00:00+00:00    19.6001
2021-12-27 14:00:00+00:00    19.3468
2021-12-27 15:00:00+00:00    19.3468
2021-12-28 06:00:00+00:00    19.7520
2021-12-28 07:00:00+00:00    19.7773
2021-12-28 08:00:00+00:00    19.5747
2021-12-28 09:00:00+00:00    19.5874
2021-12-28 10:00:00+00:00    19.5241
2021-12-28 11:00:00+00:00    19.4228
2021-12-28 12:00:00+00:00    19.3848
2021-12-28 13:00:00+00:00    19.2075
2021-12-28 14:00:00+00:00    18.9289
2021-12-28 15:00:00+00:00    18.9416
2021-12-29 06:00:00+00:00    18.9416
2021-12-29 07:00:00+00:00    18.8657
2021-12-29 08:00:00+00:00    19.2202
2021-12-29 09:00:00+00:00    19.3848
2021-12-29 10:00:00+00:00    19.5747
2021-12-29 11:00:00+00:00    19.6127
2021-12-29 12:00:00+00:00    19.6507
2021-12-29 13:00:00+00:00    19.8660
2021-12-29 14:00:00+00:00    19.8533
2021-12-29 15:00:00+00:00    19.7773
2021-12-30 06:00:00+00:00    20.0052
2021-12-30 07:00:00+00:00    19.9545
2021-12-30 08:00:00+00:00    19.9165
2021-12-30 09:00:00+00:00    19.8153
2021-12-30 10:00:00+00:00    19.7646
2021-12-30 11:00:00+00:00    19.5747
2021-12-30 12:00:00+00:00    19.6253
2021-12-30 13:00:00+00:00    19.6380
2021-12-30 14:00:00+00:00    19.4228
2021-12-30 15:00:00+00:00    19.3215
2021-12-31 06:00:00+00:00    19.3974
2021-12-31 07:00:00+00:00    19.6253
2021-12-31 08:00:00+00:00    19.8406
2021-12-31 09:00:00+00:00    19.7267
2021-12-31 10:00:00+00:00    19.4355
2021-12-31 11:00:00+00:00    19.6001
2021-12-31 12:00:00+00:00    19.4481
2021-12-31 13:00:00+00:00    19.3721
2021-12-31 14:00:00+00:00    19.2582
2021-12-31 15:00:00+00:00    19.5494
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    138.199997
2024-01-10 10:00:00+00:00    138.000000
2024-01-10 10:30:00+00:00    138.199997
2024-01-10 11:00:00+00:00    138.300000
2024-01-10 11:30:00+00:00    137.899994
2024-01-10 12:00:00+00:00    137.700000
2024-01-10 12:30:00+00:00    138.300003
2024-01-10 13:00:00+00:00    139.200000
2024-01-10 13:30:00+00:00    139.000000
2024-01-10 14:00:00+00:00    139.600000
2024-01-10 15:00:00+00:00    139.700000
2024-01-11 06:00:00+00:00    140.200000
2024-01-11 06:30:00+00:00    140.800003
2024-01-11 07:00:00+00:00    141.300000
2024-01-11 07:30:00+00:00    140.899994
2024-01-11 08:00:00+00:00    140.200000
2024-01-11 08:30:00+00:00    141.199997
2024-01-11 09:00:00+00:00    141.100000
2024-01-11 09:30:00+00:00    140.699997
2024-01-11 10:00:00+00:00    140.700000
2024-01-11 10:30:00+00:00    140.899994
2024-01-11 11:00:00+00:00    140.800000
2024-01-11 11:30:00+00:00    140.800003
2024-01-11 12:00:00+00:00    139.800000
2024-01-11 12:30:00+00:00    140.699997
2024-01-11 13:00:00+00:00    140.200000
2024-01-11 13:30:00+00:00    140.000000
2024-01-11 14:00:00+00:00    140.000000
2024-01-11 14:30:00+00:00    139.600006
2024-01-11 15:00:00+00:00    139.600000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    0.070300
2018-01-02 11:00:00+03:00   -0.020100
2018-01-02 12:00:00+03:00    0.010100
2018-01-02 13:00:00+03:00    0.020000
2018-01-02 14:00:00+03:00    0.030200
                               ...   
2024-01-11 13:00:00+00:00   -0.499997
2024-01-11 13:30:00+00:00   -0.200000
2024-01-11 14:00:00+00:00    0.000000
2024-01-11 14:30:00+00:00   -0.399994
2024-01-11 15:00:00+00:00   -0.000006
Name: price, Length: 19223, dtype: float64
ADF Statistic: -21.493290272406494
p-value: 0.0
Critical Values: {'1%': -3.4306909715527216, '5%': -2.8616906970881657, '10%': -2.5668502120039105}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19224
Model:                ARIMA(30, 3, 1)   Log Likelihood              -27078.046
Date:                Thu, 11 Jan 2024   AIC                          54220.091
Time:                        22:17:21   BIC                          54471.732
Sample:                             0   HQIC                         54302.579
                              - 19224                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -2.0197      0.005   -416.771      0.000      -2.029      -2.010
ar.L2         -2.3160      0.012   -188.058      0.000      -2.340      -2.292
ar.L3         -2.3633      0.020   -120.347      0.000      -2.402      -2.325
ar.L4         -2.3033      0.027    -86.812      0.000      -2.355      -2.251
ar.L5         -2.2868      0.033    -68.525      0.000      -2.352      -2.221
ar.L6         -2.2112      0.039    -56.246      0.000      -2.288      -2.134
ar.L7         -2.1679      0.044    -48.836      0.000      -2.255      -2.081
ar.L8         -2.0757      0.049    -42.089      0.000      -2.172      -1.979
ar.L9         -2.0039      0.053    -37.474      0.000      -2.109      -1.899
ar.L10        -1.8534      0.057    -32.790      0.000      -1.964      -1.743
ar.L11        -1.7079      0.059    -29.096      0.000      -1.823      -1.593
ar.L12        -1.5251      0.060    -25.418      0.000      -1.643      -1.408
ar.L13        -1.3695      0.060    -22.832      0.000      -1.487      -1.252
ar.L14        -1.2926      0.059    -21.845      0.000      -1.409      -1.177
ar.L15        -1.2250      0.058    -20.998      0.000      -1.339      -1.111
ar.L16        -1.3320      0.058    -23.100      0.000      -1.445      -1.219
ar.L17        -1.4165      0.057    -24.815      0.000      -1.528      -1.305
ar.L18        -1.0238      0.057    -17.911      0.000      -1.136      -0.912
ar.L19        -0.2244      0.056     -3.988      0.000      -0.335      -0.114
ar.L20         0.0299      0.053      0.562      0.574      -0.074       0.134
ar.L21         0.0124      0.049      0.252      0.801      -0.084       0.109
ar.L22         0.0307      0.045      0.676      0.499      -0.058       0.120
ar.L23        -0.0206      0.041     -0.497      0.619      -0.102       0.060
ar.L24        -0.0188      0.038     -0.500      0.617      -0.092       0.055
ar.L25        -0.0353      0.034     -1.047      0.295      -0.101       0.031
ar.L26         0.0220      0.029      0.759      0.448      -0.035       0.079
ar.L27         0.0712      0.024      2.934      0.003       0.024       0.119
ar.L28         0.1303      0.020      6.654      0.000       0.092       0.169
ar.L29         0.1099      0.015      7.553      0.000       0.081       0.138
ar.L30         0.0823      0.007     12.147      0.000       0.069       0.096
ma.L1         -0.9566      0.004   -269.379      0.000      -0.964      -0.950
sigma2         1.0508      0.005    208.251      0.000       1.041       1.061
===================================================================================
Ljung-Box (L1) (Q):                  29.90   Jarque-Bera (JB):            172261.02
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):              99.68   Skew:                            -0.43
Prob(H) (two-sided):                  0.00   Kurtosis:                        17.64
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224    140.178317
19225    140.336548
19226    140.131035
19227    139.507689
19228    139.849302
19229    140.153843
19230    139.528810
19231    139.486655
19232    139.486598
19233    139.646203
Name: predicted_mean, dtype: float64
       lower price  upper price
19224   138.169167   142.187467
19225   138.326833   142.346263
19226   137.599203   142.662867
19227   136.907643   142.107734
19228   136.888209   142.810394
19229   137.089550   143.218137
19230   136.105560   142.952060
19231   135.943273   143.030038
19232   135.563477   143.409718
19233   135.581079   143.711327

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00   9.3416      TTKOM
2021-12-27 10:00:00+03:00   9.3416      TTKOM
2021-12-27 11:00:00+03:00   9.3061      TTKOM
2021-12-27 12:00:00+03:00   9.2795      TTKOM
2021-12-27 13:00:00+03:00   9.3061      TTKOM
...                            ...        ...
2024-01-11 14:00:00+03:00  27.3200      TTKOM
2024-01-11 15:00:00+03:00  26.9600      TTKOM
2024-01-11 16:00:00+03:00  26.8400      TTKOM
2024-01-11 17:00:00+03:00  26.7800      TTKOM
2024-01-11 18:00:00+03:00  26.7800      TTKOM

[5104 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    9.3416
2021-12-27 07:00:00+00:00    9.3416
2021-12-27 08:00:00+00:00    9.3061
2021-12-27 09:00:00+00:00    9.2795
2021-12-27 10:00:00+00:00    9.3061
2021-12-27 11:00:00+00:00    9.1732
2021-12-27 12:00:00+00:00    9.0934
2021-12-27 13:00:00+00:00    9.1554
2021-12-27 14:00:00+00:00    9.1377
2021-12-27 15:00:00+00:00    9.1200
2021-12-28 06:00:00+00:00    9.1023
2021-12-28 07:00:00+00:00    9.2086
2021-12-28 08:00:00+00:00    9.1998
2021-12-28 09:00:00+00:00    9.1466
2021-12-28 10:00:00+00:00    9.1377
2021-12-28 11:00:00+00:00    9.0402
2021-12-28 12:00:00+00:00    9.0668
2021-12-28 13:00:00+00:00    8.9605
2021-12-28 14:00:00+00:00    8.7566
2021-12-28 15:00:00+00:00    8.7300
2021-12-29 06:00:00+00:00    8.7123
2021-12-29 07:00:00+00:00    8.7212
2021-12-29 08:00:00+00:00    8.7832
2021-12-29 09:00:00+00:00    8.8364
2021-12-29 10:00:00+00:00    8.8187
2021-12-29 11:00:00+00:00    8.7655
2021-12-29 12:00:00+00:00    8.8541
2021-12-29 13:00:00+00:00    8.8630
2021-12-29 14:00:00+00:00    8.9073
2021-12-29 15:00:00+00:00    8.8896
2021-12-30 06:00:00+00:00    8.9605
2021-12-30 07:00:00+00:00    8.8541
2021-12-30 08:00:00+00:00    8.8807
2021-12-30 09:00:00+00:00    8.8364
2021-12-30 10:00:00+00:00    8.8275
2021-12-30 11:00:00+00:00    8.7212
2021-12-30 12:00:00+00:00    8.7655
2021-12-30 13:00:00+00:00    8.7655
2021-12-30 14:00:00+00:00    8.6768
2021-12-30 15:00:00+00:00    8.6680
2021-12-31 06:00:00+00:00    8.6414
2021-12-31 07:00:00+00:00    8.6148
2021-12-31 08:00:00+00:00    8.6325
2021-12-31 09:00:00+00:00    8.6591
2021-12-31 10:00:00+00:00    8.5971
2021-12-31 11:00:00+00:00    8.6325
2021-12-31 12:00:00+00:00    8.5528
2021-12-31 13:00:00+00:00    8.5528
2021-12-31 14:00:00+00:00    8.5173
2021-12-31 15:00:00+00:00    8.5350
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    27.180000
2024-01-10 10:00:00+00:00    27.380000
2024-01-10 10:30:00+00:00    27.400000
2024-01-10 11:00:00+00:00    27.380000
2024-01-10 11:30:00+00:00    27.360001
2024-01-10 12:00:00+00:00    27.160000
2024-01-10 12:30:00+00:00    27.219999
2024-01-10 13:00:00+00:00    27.260000
2024-01-10 13:30:00+00:00    27.219999
2024-01-10 14:00:00+00:00    27.720000
2024-01-10 15:00:00+00:00    27.600000
2024-01-11 06:00:00+00:00    27.700000
2024-01-11 06:30:00+00:00    27.639999
2024-01-11 07:00:00+00:00    27.560000
2024-01-11 07:30:00+00:00    27.480000
2024-01-11 08:00:00+00:00    27.340000
2024-01-11 08:30:00+00:00    27.360001
2024-01-11 09:00:00+00:00    27.400000
2024-01-11 09:30:00+00:00    27.340000
2024-01-11 10:00:00+00:00    27.360000
2024-01-11 10:30:00+00:00    27.299999
2024-01-11 11:00:00+00:00    27.320000
2024-01-11 11:30:00+00:00    27.260000
2024-01-11 12:00:00+00:00    26.960000
2024-01-11 12:30:00+00:00    27.219999
2024-01-11 13:00:00+00:00    26.840000
2024-01-11 13:30:00+00:00    26.740000
2024-01-11 14:00:00+00:00    26.780000
2024-01-11 14:30:00+00:00    26.780001
2024-01-11 15:00:00+00:00    26.780000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    1.123000e-01
2018-01-02 11:00:00+03:00    0.000000e+00
2018-01-02 12:00:00+03:00   -1.610000e-02
2018-01-02 13:00:00+03:00    0.000000e+00
2018-01-02 14:00:00+03:00    2.410000e-02
                                 ...     
2024-01-11 13:00:00+00:00   -3.799993e-01
2024-01-11 13:30:00+00:00   -1.000002e-01
2024-01-11 14:00:00+00:00    4.000023e-02
2024-01-11 14:30:00+00:00    6.866455e-07
2024-01-11 15:00:00+00:00   -6.866455e-07
Name: price, Length: 19223, dtype: float64
ADF Statistic: -20.3331771765092
p-value: 0.0
Critical Values: {'1%': -3.4306910248977847, '5%': -2.8616907206633995, '10%': -2.5668502245526077}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19224
Model:                ARIMA(30, 3, 1)   Log Likelihood               12036.130
Date:                Thu, 11 Jan 2024   AIC                         -24008.261
Time:                        22:20:30   BIC                         -23756.621
Sample:                             0   HQIC                        -23925.773
                              - 19224                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.2045      0.004   -323.384      0.000      -1.212      -1.197
ar.L2         -1.1658      0.007   -157.840      0.000      -1.180      -1.151
ar.L3         -1.2179      0.010   -121.634      0.000      -1.237      -1.198
ar.L4         -1.1723      0.013    -91.897      0.000      -1.197      -1.147
ar.L5         -1.1992      0.015    -80.086      0.000      -1.229      -1.170
ar.L6         -1.1307      0.017    -66.630      0.000      -1.164      -1.097
ar.L7         -1.1569      0.018    -62.619      0.000      -1.193      -1.121
ar.L8         -1.0875      0.020    -54.420      0.000      -1.127      -1.048
ar.L9         -1.0710      0.021    -50.523      0.000      -1.113      -1.029
ar.L10        -0.9834      0.022    -44.176      0.000      -1.027      -0.940
ar.L11        -0.9440      0.023    -41.099      0.000      -0.989      -0.899
ar.L12        -0.8655      0.024    -36.763      0.000      -0.912      -0.819
ar.L13        -0.8157      0.024    -34.224      0.000      -0.862      -0.769
ar.L14        -0.7620      0.023    -32.437      0.000      -0.808      -0.716
ar.L15        -0.7125      0.023    -30.690      0.000      -0.758      -0.667
ar.L16        -0.7397      0.023    -32.345      0.000      -0.784      -0.695
ar.L17        -0.8757      0.023    -38.703      0.000      -0.920      -0.831
ar.L18        -0.4123      0.023    -18.005      0.000      -0.457      -0.367
ar.L19        -0.4375      0.022    -19.759      0.000      -0.481      -0.394
ar.L20        -0.5171      0.021    -24.232      0.000      -0.559      -0.475
ar.L21        -0.4826      0.021    -23.503      0.000      -0.523      -0.442
ar.L22        -0.4608      0.020    -23.505      0.000      -0.499      -0.422
ar.L23        -0.3805      0.019    -20.338      0.000      -0.417      -0.344
ar.L24        -0.3369      0.017    -19.359      0.000      -0.371      -0.303
ar.L25        -0.2544      0.016    -15.863      0.000      -0.286      -0.223
ar.L26        -0.1988      0.015    -13.636      0.000      -0.227      -0.170
ar.L27        -0.1476      0.013    -11.506      0.000      -0.173      -0.122
ar.L28        -0.1246      0.011    -11.674      0.000      -0.146      -0.104
ar.L29        -0.0796      0.009     -9.354      0.000      -0.096      -0.063
ar.L30        -0.0307      0.005     -6.040      0.000      -0.041      -0.021
ma.L1         -0.9583      0.003   -373.074      0.000      -0.963      -0.953
sigma2         0.0167   5.87e-05    284.317      0.000       0.017       0.017
===================================================================================
Ljung-Box (L1) (Q):                   1.00   Jarque-Bera (JB):            363205.87
Prob(Q):                              0.32   Prob(JB):                         0.00
Heteroskedasticity (H):              16.47   Skew:                             0.62
Prob(H) (two-sided):                  0.00   Kurtosis:                        24.26
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224    26.761062
19225    26.657897
19226    26.676648
19227    26.532380
19228    26.566860
19229    26.502741
19230    26.448061
19231    26.378880
19232    26.316081
19233    26.286939
Name: predicted_mean, dtype: float64
       lower price  upper price
19224    26.507847    27.014278
19225    26.327652    26.988141
19226    26.267934    27.085363
19227    26.063053    27.001707
19228    26.031233    27.102487
19229    25.910584    27.094899
19230    25.790631    27.105491
19231    25.665460    27.092300
19232    25.536983    27.095180
19233    25.447280    27.126599

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00  18.8440      TCELL
2021-12-27 10:00:00+03:00  18.9601      TCELL
2021-12-27 11:00:00+03:00  18.7569      TCELL
2021-12-27 12:00:00+03:00  18.7182      TCELL
2021-12-27 13:00:00+03:00  18.7763      TCELL
...                            ...        ...
2024-01-11 14:00:00+03:00  60.9000      TCELL
2024-01-11 15:00:00+03:00  60.6500      TCELL
2024-01-11 16:00:00+03:00  60.8000      TCELL
2024-01-11 17:00:00+03:00  60.8500      TCELL
2024-01-11 18:00:00+03:00  60.8500      TCELL

[5104 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    18.8440
2021-12-27 07:00:00+00:00    18.9601
2021-12-27 08:00:00+00:00    18.7569
2021-12-27 09:00:00+00:00    18.7182
2021-12-27 10:00:00+00:00    18.7763
2021-12-27 11:00:00+00:00    18.2536
2021-12-27 12:00:00+00:00    18.3310
2021-12-27 13:00:00+00:00    18.5343
2021-12-27 14:00:00+00:00    18.4085
2021-12-27 15:00:00+00:00    18.2923
2021-12-28 06:00:00+00:00    18.4569
2021-12-28 07:00:00+00:00    18.6988
2021-12-28 08:00:00+00:00    18.6020
2021-12-28 09:00:00+00:00    18.6311
2021-12-28 10:00:00+00:00    18.5633
2021-12-28 11:00:00+00:00    18.5246
2021-12-28 12:00:00+00:00    18.5343
2021-12-28 13:00:00+00:00    18.5246
2021-12-28 14:00:00+00:00    18.0988
2021-12-28 15:00:00+00:00    18.0020
2021-12-29 06:00:00+00:00    18.0020
2021-12-29 07:00:00+00:00    18.1665
2021-12-29 08:00:00+00:00    18.3891
2021-12-29 09:00:00+00:00    18.3891
2021-12-29 10:00:00+00:00    18.3794
2021-12-29 11:00:00+00:00    18.4085
2021-12-29 12:00:00+00:00    18.7375
2021-12-29 13:00:00+00:00    18.9408
2021-12-29 14:00:00+00:00    18.9698
2021-12-29 15:00:00+00:00    18.9698
2021-12-30 06:00:00+00:00    19.1053
2021-12-30 07:00:00+00:00    18.7956
2021-12-30 08:00:00+00:00    18.7763
2021-12-30 09:00:00+00:00    18.6988
2021-12-30 10:00:00+00:00    18.7375
2021-12-30 11:00:00+00:00    18.4085
2021-12-30 12:00:00+00:00    18.4569
2021-12-30 13:00:00+00:00    18.4665
2021-12-30 14:00:00+00:00    18.3698
2021-12-30 15:00:00+00:00    18.1859
2021-12-31 06:00:00+00:00    18.2536
2021-12-31 07:00:00+00:00    18.5633
2021-12-31 08:00:00+00:00    18.6988
2021-12-31 09:00:00+00:00    18.7375
2021-12-31 10:00:00+00:00    18.4569
2021-12-31 11:00:00+00:00    18.4472
2021-12-31 12:00:00+00:00    18.3504
2021-12-31 13:00:00+00:00    18.2827
2021-12-31 14:00:00+00:00    17.3535
2021-12-31 15:00:00+00:00    17.8665
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    61.200001
2024-01-10 10:00:00+00:00    61.550000
2024-01-10 10:30:00+00:00    61.500000
2024-01-10 11:00:00+00:00    61.850000
2024-01-10 11:30:00+00:00    61.549999
2024-01-10 12:00:00+00:00    61.350000
2024-01-10 12:30:00+00:00    61.500000
2024-01-10 13:00:00+00:00    61.850000
2024-01-10 13:30:00+00:00    61.549999
2024-01-10 14:00:00+00:00    61.600000
2024-01-10 15:00:00+00:00    61.500000
2024-01-11 06:00:00+00:00    61.850000
2024-01-11 06:30:00+00:00    61.599998
2024-01-11 07:00:00+00:00    61.150000
2024-01-11 07:30:00+00:00    61.250000
2024-01-11 08:00:00+00:00    60.900000
2024-01-11 08:30:00+00:00    61.049999
2024-01-11 09:00:00+00:00    61.000000
2024-01-11 09:30:00+00:00    60.849998
2024-01-11 10:00:00+00:00    60.700000
2024-01-11 10:30:00+00:00    60.599998
2024-01-11 11:00:00+00:00    60.900000
2024-01-11 11:30:00+00:00    61.049999
2024-01-11 12:00:00+00:00    60.650000
2024-01-11 12:30:00+00:00    61.250000
2024-01-11 13:00:00+00:00    60.800000
2024-01-11 13:30:00+00:00    60.750000
2024-01-11 14:00:00+00:00    60.850000
2024-01-11 14:30:00+00:00    60.849998
2024-01-11 15:00:00+00:00    60.850000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    0.039200
2018-01-02 11:00:00+03:00   -0.101800
2018-01-02 12:00:00+03:00   -0.007700
2018-01-02 13:00:00+03:00    0.054800
2018-01-02 14:00:00+03:00    0.054700
                               ...   
2024-01-11 13:00:00+00:00   -0.450000
2024-01-11 13:30:00+00:00   -0.050000
2024-01-11 14:00:00+00:00    0.100000
2024-01-11 14:30:00+00:00   -0.000002
2024-01-11 15:00:00+00:00    0.000002
Name: price, Length: 19223, dtype: float64
ADF Statistic: -21.803024085201393
p-value: 0.0
Critical Values: {'1%': -3.4306910248977847, '5%': -2.8616907206633995, '10%': -2.5668502245526077}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19224
Model:                ARIMA(30, 1, 1)   Log Likelihood                1917.768
Date:                Thu, 11 Jan 2024   AIC                          -3771.536
Time:                        22:23:17   BIC                          -3519.892
Sample:                             0   HQIC                         -3689.048
                              - 19224                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.0724      0.007   -155.073      0.000      -1.086      -1.059
ar.L2         -0.1235      0.005    -24.672      0.000      -0.133      -0.114
ar.L3         -0.0317      0.005     -6.471      0.000      -0.041      -0.022
ar.L4         -0.0336      0.006     -5.707      0.000      -0.045      -0.022
ar.L5         -0.0257      0.007     -3.937      0.000      -0.038      -0.013
ar.L6         -0.0117      0.007     -1.738      0.082      -0.025       0.001
ar.L7         -0.0268      0.007     -3.761      0.000      -0.041      -0.013
ar.L8         -0.0276      0.007     -3.843      0.000      -0.042      -0.013
ar.L9         -0.0013      0.007     -0.189      0.850      -0.015       0.012
ar.L10         0.0048      0.007      0.678      0.498      -0.009       0.018
ar.L11         0.0253      0.007      3.795      0.000       0.012       0.038
ar.L12         0.0003      0.007      0.040      0.968      -0.013       0.014
ar.L13        -0.0039      0.007     -0.578      0.563      -0.017       0.009
ar.L14         0.0022      0.006      0.370      0.712      -0.010       0.014
ar.L15         0.0065      0.006      1.103      0.270      -0.005       0.018
ar.L16        -0.0197      0.006     -3.304      0.001      -0.031      -0.008
ar.L17        -0.0482      0.005     -9.013      0.000      -0.059      -0.038
ar.L18         0.1191      0.005     23.008      0.000       0.109       0.129
ar.L19         0.1058      0.004     23.736      0.000       0.097       0.115
ar.L20        -0.0719      0.005    -13.352      0.000      -0.082      -0.061
ar.L21        -0.0717      0.006    -12.934      0.000      -0.083      -0.061
ar.L22        -0.0346      0.006     -5.873      0.000      -0.046      -0.023
ar.L23         0.0011      0.006      0.172      0.863      -0.011       0.013
ar.L24         0.0143      0.006      2.377      0.017       0.003       0.026
ar.L25         0.0131      0.007      2.006      0.045       0.000       0.026
ar.L26         0.0121      0.007      1.737      0.082      -0.002       0.026
ar.L27         0.0038      0.007      0.541      0.588      -0.010       0.018
ar.L28        -0.0010      0.007     -0.145      0.884      -0.015       0.013
ar.L29        -0.0084      0.007     -1.167      0.243      -0.023       0.006
ar.L30         0.0301      0.006      5.448      0.000       0.019       0.041
ma.L1          0.8963      0.006    149.080      0.000       0.884       0.908
sigma2         0.0482      0.000    273.966      0.000       0.048       0.049
===================================================================================
Ljung-Box (L1) (Q):                   0.24   Jarque-Bera (JB):            209404.58
Prob(Q):                              0.62   Prob(JB):                         0.00
Heteroskedasticity (H):              18.31   Skew:                             0.63
Prob(H) (two-sided):                  0.00   Kurtosis:                        19.12
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224    60.846388
19225    60.764522
19226    60.826806
19227    60.798679
19228    60.823256
19229    60.859869
19230    60.809053
19231    60.832872
19232    60.764996
19233    60.879722
Name: predicted_mean, dtype: float64
       lower price  upper price
19224    60.416111    61.276665
19225    60.207033    61.322012
19226    60.150649    61.502962
19227    60.038149    61.559209
19228    59.977678    61.668834
19229    59.947941    61.771797
19230    59.826403    61.791703
19231    59.795556    61.870188
19232    59.668177    61.861816
19233    59.732729    62.026715
                           price short_name
timestamp                                  
2021-12-27 09:00:00+03:00   4.78      HALKB
2021-12-27 10:00:00+03:00   4.80      HALKB
2021-12-27 11:00:00+03:00   4.78      HALKB
2021-12-27 12:00:00+03:00   4.78      HALKB
2021-12-27 13:00:00+03:00   4.78      HALKB
...                          ...        ...
2024-01-11 14:00:00+03:00  13.15      HALKB
2024-01-11 15:00:00+03:00  12.99      HALKB
2024-01-11 16:00:00+03:00  12.98      HALKB
2024-01-11 17:00:00+03:00  13.02      HALKB
2024-01-11 18:00:00+03:00  13.02      HALKB

[5104 rows x 2 columns]

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    4.78
2021-12-27 07:00:00+00:00    4.80
2021-12-27 08:00:00+00:00    4.78
2021-12-27 09:00:00+00:00    4.78
2021-12-27 10:00:00+00:00    4.78
2021-12-27 11:00:00+00:00    4.74
2021-12-27 12:00:00+00:00    4.73
2021-12-27 13:00:00+00:00    4.74
2021-12-27 14:00:00+00:00    4.70
2021-12-27 15:00:00+00:00    4.70
2021-12-28 06:00:00+00:00    4.71
2021-12-28 07:00:00+00:00    4.67
2021-12-28 08:00:00+00:00    4.65
2021-12-28 09:00:00+00:00    4.66
2021-12-28 10:00:00+00:00    4.66
2021-12-28 11:00:00+00:00    4.64
2021-12-28 12:00:00+00:00    4.65
2021-12-28 13:00:00+00:00    4.64
2021-12-28 14:00:00+00:00    4.57
2021-12-28 15:00:00+00:00    4.58
2021-12-29 06:00:00+00:00    4.58
2021-12-29 07:00:00+00:00    4.55
2021-12-29 08:00:00+00:00    4.61
2021-12-29 09:00:00+00:00    4.58
2021-12-29 10:00:00+00:00    4.60
2021-12-29 11:00:00+00:00    4.59
2021-12-29 12:00:00+00:00    4.66
2021-12-29 13:00:00+00:00    4.65
2021-12-29 14:00:00+00:00    4.63
2021-12-29 15:00:00+00:00    4.64
2021-12-30 06:00:00+00:00    4.65
2021-12-30 07:00:00+00:00    4.65
2021-12-30 08:00:00+00:00    4.65
2021-12-30 09:00:00+00:00    4.64
2021-12-30 10:00:00+00:00    4.68
2021-12-30 11:00:00+00:00    4.63
2021-12-30 12:00:00+00:00    4.63
2021-12-30 13:00:00+00:00    4.62
2021-12-30 14:00:00+00:00    4.58
2021-12-30 15:00:00+00:00    4.58
2021-12-31 06:00:00+00:00    4.58
2021-12-31 07:00:00+00:00    4.58
2021-12-31 08:00:00+00:00    4.61
2021-12-31 09:00:00+00:00    4.60
2021-12-31 10:00:00+00:00    4.57
2021-12-31 11:00:00+00:00    4.56
2021-12-31 12:00:00+00:00    4.55
2021-12-31 13:00:00+00:00    4.55
2021-12-31 14:00:00+00:00    4.50
2021-12-31 15:00:00+00:00    4.51
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    13.28
2024-01-10 10:00:00+00:00    13.26
2024-01-10 10:30:00+00:00    13.33
2024-01-10 11:00:00+00:00    13.29
2024-01-10 11:30:00+00:00    13.29
2024-01-10 12:00:00+00:00    13.20
2024-01-10 12:30:00+00:00    13.22
2024-01-10 13:00:00+00:00    13.31
2024-01-10 13:30:00+00:00    13.29
2024-01-10 14:00:00+00:00    13.27
2024-01-10 15:00:00+00:00    13.22
2024-01-11 06:00:00+00:00    13.25
2024-01-11 06:30:00+00:00    13.39
2024-01-11 07:00:00+00:00    13.27
2024-01-11 07:30:00+00:00    13.20
2024-01-11 08:00:00+00:00    13.19
2024-01-11 08:30:00+00:00    13.22
2024-01-11 09:00:00+00:00    13.21
2024-01-11 09:30:00+00:00    13.18
2024-01-11 10:00:00+00:00    13.13
2024-01-11 10:30:00+00:00    13.13
2024-01-11 11:00:00+00:00    13.15
2024-01-11 11:30:00+00:00    13.11
2024-01-11 12:00:00+00:00    12.99
2024-01-11 12:30:00+00:00    13.07
2024-01-11 13:00:00+00:00    12.98
2024-01-11 13:30:00+00:00    12.96
2024-01-11 14:00:00+00:00    13.02
2024-01-11 14:30:00+00:00    13.02
2024-01-11 15:00:00+00:00    13.02
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    3.219000e-01
2018-01-02 11:00:00+03:00   -1.950000e-02
2018-01-02 12:00:00+03:00   -9.760000e-02
2018-01-02 13:00:00+03:00    1.950000e-02
2018-01-02 14:00:00+03:00    5.860000e-02
                                 ...     
2024-01-11 13:00:00+00:00   -8.999969e-02
2024-01-11 13:30:00+00:00   -1.999996e-02
2024-01-11 14:00:00+00:00    5.999996e-02
2024-01-11 14:30:00+00:00    4.577637e-07
2024-01-11 15:00:00+00:00   -4.577637e-07
Name: price, Length: 19222, dtype: float64
ADF Statistic: -20.568098322137963
p-value: 0.0
Critical Values: {'1%': -3.430690935998617, '5%': -2.8616906813754404, '10%': -2.5668502036402927}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19223
Model:                ARIMA(30, 1, 1)   Log Likelihood               22499.887
Date:                Thu, 11 Jan 2024   AIC                         -44935.775
Time:                        22:24:10   BIC                         -44684.133
Sample:                             0   HQIC                        -44853.287
                              - 19223                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.0020      0.325     -0.006      0.995      -0.638       0.634
ar.L2          0.0337      0.003      9.852      0.000       0.027       0.040
ar.L3          0.0162      0.012      1.325      0.185      -0.008       0.040
ar.L4         -0.0089      0.007     -1.221      0.222      -0.023       0.005
ar.L5          0.0081      0.006      1.433      0.152      -0.003       0.019
ar.L6          0.0211      0.006      3.615      0.000       0.010       0.033
ar.L7         -0.0064      0.009     -0.722      0.470      -0.024       0.011
ar.L8          0.0037      0.005      0.696      0.486      -0.007       0.014
ar.L9         -0.0018      0.006     -0.285      0.776      -0.014       0.011
ar.L10         0.0029      0.006      0.498      0.619      -0.008       0.014
ar.L11         0.0063      0.005      1.162      0.245      -0.004       0.017
ar.L12        -0.0068      0.006     -1.083      0.279      -0.019       0.005
ar.L13        -0.0109      0.005     -2.021      0.043      -0.021      -0.000
ar.L14         0.0143      0.006      2.268      0.023       0.002       0.027
ar.L15         0.0060      0.007      0.875      0.382      -0.007       0.019
ar.L16        -0.0119      0.004     -2.818      0.005      -0.020      -0.004
ar.L17        -0.0046      0.005     -0.848      0.396      -0.015       0.006
ar.L18         0.0101      0.003      2.942      0.003       0.003       0.017
ar.L19         0.0308      0.004      6.919      0.000       0.022       0.040
ar.L20         0.0191      0.011      1.737      0.082      -0.002       0.041
ar.L21        -0.0053      0.008     -0.674      0.501      -0.021       0.010
ar.L22        -0.0057      0.005     -1.167      0.243      -0.015       0.004
ar.L23         0.0011      0.005      0.207      0.836      -0.009       0.012
ar.L24         0.0077      0.005      1.484      0.138      -0.002       0.018
ar.L25         0.0148      0.006      2.381      0.017       0.003       0.027
ar.L26        -0.0135      0.008     -1.711      0.087      -0.029       0.002
ar.L27         0.0115      0.008      1.483      0.138      -0.004       0.027
ar.L28         0.0079      0.007      1.093      0.275      -0.006       0.022
ar.L29        -0.0011      0.007     -0.169      0.866      -0.014       0.012
ar.L30         0.0179      0.005      3.601      0.000       0.008       0.028
ma.L1         -0.0022      0.325     -0.007      0.995      -0.638       0.634
sigma2         0.0056   1.12e-05    502.973      0.000       0.006       0.006
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           4060904.23
Prob(Q):                              0.99   Prob(JB):                         0.00
Heteroskedasticity (H):               3.97   Skew:                             0.77
Prob(H) (two-sided):                  0.00   Kurtosis:                        74.19
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223    13.024361
19224    13.027552
19225    13.028146
19226    13.019217
19227    13.018017
19228    13.020960
19229    13.023027
19230    13.020657
19231    13.016209
19232    13.018109
Name: predicted_mean, dtype: float64
       lower price  upper price
19223    12.877251    13.171470
19224    12.819950    13.235154
19225    12.771176    13.285115
19226    12.719739    13.318695
19227    12.681885    13.354148
19228    12.651234    13.390687
19229    12.621330    13.424723
19230    12.589685    13.451630
19231    12.557550    13.474867
19232    12.533426    13.502792

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00   3.1299      ISCTR
2021-12-27 10:00:00+03:00   3.1702      ISCTR
2021-12-27 11:00:00+03:00   3.1823      ISCTR
2021-12-27 12:00:00+03:00   3.1984      ISCTR
2021-12-27 13:00:00+03:00   3.1864      ISCTR
...                            ...        ...
2024-01-11 14:00:00+03:00  25.7000      ISCTR
2024-01-11 15:00:00+03:00  25.6600      ISCTR
2024-01-11 16:00:00+03:00  25.6400      ISCTR
2024-01-11 17:00:00+03:00  25.7400      ISCTR
2024-01-11 18:00:00+03:00  25.7200      ISCTR

[5104 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    3.1299
2021-12-27 07:00:00+00:00    3.1702
2021-12-27 08:00:00+00:00    3.1823
2021-12-27 09:00:00+00:00    3.1984
2021-12-27 10:00:00+00:00    3.1864
2021-12-27 11:00:00+00:00    3.1621
2021-12-27 12:00:00+00:00    3.1823
2021-12-27 13:00:00+00:00    3.1904
2021-12-27 14:00:00+00:00    3.2146
2021-12-27 15:00:00+00:00    3.2146
2021-12-28 06:00:00+00:00    3.1943
2021-12-28 07:00:00+00:00    3.1056
2021-12-28 08:00:00+00:00    3.0815
2021-12-28 09:00:00+00:00    3.0734
2021-12-28 10:00:00+00:00    3.0774
2021-12-28 11:00:00+00:00    3.0653
2021-12-28 12:00:00+00:00    3.0734
2021-12-28 13:00:00+00:00    3.0492
2021-12-28 14:00:00+00:00    3.0089
2021-12-28 15:00:00+00:00    3.0089
2021-12-29 06:00:00+00:00    2.9766
2021-12-29 07:00:00+00:00    2.9484
2021-12-29 08:00:00+00:00    3.0008
2021-12-29 09:00:00+00:00    2.9968
2021-12-29 10:00:00+00:00    3.0008
2021-12-29 11:00:00+00:00    3.0049
2021-12-29 12:00:00+00:00    3.0250
2021-12-29 13:00:00+00:00    3.0411
2021-12-29 14:00:00+00:00    3.0169
2021-12-29 15:00:00+00:00    3.0089
2021-12-30 06:00:00+00:00    3.0209
2021-12-30 07:00:00+00:00    3.0452
2021-12-30 08:00:00+00:00    3.0613
2021-12-30 09:00:00+00:00    3.0573
2021-12-30 10:00:00+00:00    3.0653
2021-12-30 11:00:00+00:00    3.0330
2021-12-30 12:00:00+00:00    3.0330
2021-12-30 13:00:00+00:00    3.0371
2021-12-30 14:00:00+00:00    2.9887
2021-12-30 15:00:00+00:00    2.9766
2021-12-31 06:00:00+00:00    2.9928
2021-12-31 07:00:00+00:00    2.9847
2021-12-31 08:00:00+00:00    2.9968
2021-12-31 09:00:00+00:00    3.0049
2021-12-31 10:00:00+00:00    2.9766
2021-12-31 11:00:00+00:00    2.9725
2021-12-31 12:00:00+00:00    2.9565
2021-12-31 13:00:00+00:00    2.9282
2021-12-31 14:00:00+00:00    2.8959
2021-12-31 15:00:00+00:00    2.9000
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    25.100000
2024-01-10 10:00:00+00:00    25.120000
2024-01-10 10:30:00+00:00    25.260000
2024-01-10 11:00:00+00:00    25.260000
2024-01-10 11:30:00+00:00    25.260000
2024-01-10 12:00:00+00:00    25.040000
2024-01-10 12:30:00+00:00    25.219999
2024-01-10 13:00:00+00:00    25.520000
2024-01-10 13:30:00+00:00    25.600000
2024-01-10 14:00:00+00:00    25.740000
2024-01-10 15:00:00+00:00    25.700000
2024-01-11 06:00:00+00:00    25.860000
2024-01-11 06:30:00+00:00    25.760000
2024-01-11 07:00:00+00:00    25.460000
2024-01-11 07:30:00+00:00    25.480000
2024-01-11 08:00:00+00:00    25.500000
2024-01-11 08:30:00+00:00    25.540001
2024-01-11 09:00:00+00:00    25.580000
2024-01-11 09:30:00+00:00    25.680000
2024-01-11 10:00:00+00:00    25.660000
2024-01-11 10:30:00+00:00    25.879999
2024-01-11 11:00:00+00:00    25.700000
2024-01-11 11:30:00+00:00    25.700001
2024-01-11 12:00:00+00:00    25.660000
2024-01-11 12:30:00+00:00    25.820000
2024-01-11 13:00:00+00:00    25.640000
2024-01-11 13:30:00+00:00    25.660000
2024-01-11 14:00:00+00:00    25.740000
2024-01-11 14:30:00+00:00    25.719999
2024-01-11 15:00:00+00:00    25.720000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    1.130000e-02
2018-01-02 11:00:00+03:00    0.000000e+00
2018-01-02 12:00:00+03:00   -7.500000e-03
2018-01-02 13:00:00+03:00    0.000000e+00
2018-01-02 14:00:00+03:00    3.000000e-02
                                 ...     
2024-01-11 13:00:00+00:00   -1.799997e-01
2024-01-11 13:30:00+00:00    1.999985e-02
2024-01-11 14:00:00+00:00    8.000015e-02
2024-01-11 14:30:00+00:00   -2.000069e-02
2024-01-11 15:00:00+00:00    6.866455e-07
Name: price, Length: 19223, dtype: float64
ADF Statistic: -20.20767182047305
p-value: 0.0
Critical Values: {'1%': -3.4306910071142425, '5%': -2.8616907128041684, '10%': -2.5668502203692722}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19224
Model:                ARIMA(30, 1, 1)   Log Likelihood               12517.087
Date:                Thu, 11 Jan 2024   AIC                         -24970.173
Time:                        22:26:46   BIC                         -24718.530
Sample:                             0   HQIC                        -24887.685
                              - 19224                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.5987      0.094     -6.400      0.000      -0.782      -0.415
ar.L2          0.0870      0.060      1.445      0.148      -0.031       0.205
ar.L3         -0.0670      0.008     -8.828      0.000      -0.082      -0.052
ar.L4          0.0010      0.008      0.130      0.896      -0.015       0.017
ar.L5         -0.0458      0.005     -8.624      0.000      -0.056      -0.035
ar.L6          0.0235      0.007      3.267      0.001       0.009       0.038
ar.L7         -0.0191      0.006     -2.946      0.003      -0.032      -0.006
ar.L8          0.0233      0.007      3.405      0.001       0.010       0.037
ar.L9         -0.0138      0.007     -1.980      0.048      -0.028      -0.000
ar.L10         0.0542      0.006      8.447      0.000       0.042       0.067
ar.L11         0.0173      0.008      2.156      0.031       0.002       0.033
ar.L12        -0.0118      0.007     -1.676      0.094      -0.026       0.002
ar.L13         0.0156      0.006      2.670      0.008       0.004       0.027
ar.L14         0.0065      0.005      1.194      0.232      -0.004       0.017
ar.L15         0.0159      0.005      3.230      0.001       0.006       0.026
ar.L16        -0.1312      0.005    -27.847      0.000      -0.140      -0.122
ar.L17        -0.0695      0.013     -5.499      0.000      -0.094      -0.045
ar.L18         0.2758      0.008     35.756      0.000       0.261       0.291
ar.L19         0.2992      0.026     11.665      0.000       0.249       0.350
ar.L20        -0.1516      0.029     -5.180      0.000      -0.209      -0.094
ar.L21        -0.0675      0.014     -4.935      0.000      -0.094      -0.041
ar.L22         0.0194      0.009      2.275      0.023       0.003       0.036
ar.L23        -0.0415      0.005     -8.497      0.000      -0.051      -0.032
ar.L24         0.0127      0.007      1.926      0.054      -0.000       0.026
ar.L25        -0.0182      0.006     -3.105      0.002      -0.030      -0.007
ar.L26         0.0478      0.006      7.896      0.000       0.036       0.060
ar.L27        -0.0097      0.008     -1.210      0.226      -0.025       0.006
ar.L28         0.0072      0.007      1.090      0.276      -0.006       0.020
ar.L29        -0.0424      0.006     -7.055      0.000      -0.054      -0.031
ar.L30         0.0604      0.008      7.436      0.000       0.045       0.076
ma.L1         -0.0420      0.094     -0.449      0.653      -0.226       0.142
sigma2         0.0160   6.43e-05    248.687      0.000       0.016       0.016
===================================================================================
Ljung-Box (L1) (Q):                   0.06   Jarque-Bera (JB):            243151.50
Prob(Q):                              0.80   Prob(JB):                         0.00
Heteroskedasticity (H):              99.96   Skew:                             0.22
Prob(H) (two-sided):                  0.00   Kurtosis:                        20.42
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19224    25.765533
19225    25.573763
19226    25.613909
19227    25.642792
19228    25.667359
19229    25.650499
19230    25.670518
19231    25.702894
19232    25.735713
19233    25.726259
Name: predicted_mean, dtype: float64
       lower price  upper price
19224    25.517605    26.013460
19225    25.310319    25.837207
19226    25.279649    25.948168
19227    25.292285    25.993299
19228    25.270304    26.064415
19229    25.238497    26.062502
19230    25.219789    26.121247
19231    25.238170    26.167618
19232    25.236425    26.235001
19233    25.213990    26.238528
                           price short_name
timestamp                                  
2021-12-27 09:00:00+03:00   3.86      VAKBN
2021-12-27 10:00:00+03:00   3.87      VAKBN
2021-12-27 11:00:00+03:00   3.86      VAKBN
2021-12-27 12:00:00+03:00   3.86      VAKBN
2021-12-27 13:00:00+03:00   3.85      VAKBN
...                          ...        ...
2024-01-11 14:00:00+03:00  14.70      VAKBN
2024-01-11 15:00:00+03:00  14.55      VAKBN
2024-01-11 16:00:00+03:00  14.61      VAKBN
2024-01-11 17:00:00+03:00  14.68      VAKBN
2024-01-11 18:00:00+03:00  14.60      VAKBN

[5104 rows x 2 columns]

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    3.86
2021-12-27 07:00:00+00:00    3.87
2021-12-27 08:00:00+00:00    3.86
2021-12-27 09:00:00+00:00    3.86
2021-12-27 10:00:00+00:00    3.85
2021-12-27 11:00:00+00:00    3.83
2021-12-27 12:00:00+00:00    3.84
2021-12-27 13:00:00+00:00    3.86
2021-12-27 14:00:00+00:00    3.83
2021-12-27 15:00:00+00:00    3.83
2021-12-28 06:00:00+00:00    3.84
2021-12-28 07:00:00+00:00    3.78
2021-12-28 08:00:00+00:00    3.78
2021-12-28 09:00:00+00:00    3.77
2021-12-28 10:00:00+00:00    3.77
2021-12-28 11:00:00+00:00    3.77
2021-12-28 12:00:00+00:00    3.78
2021-12-28 13:00:00+00:00    3.76
2021-12-28 14:00:00+00:00    3.68
2021-12-28 15:00:00+00:00    3.69
2021-12-29 06:00:00+00:00    3.65
2021-12-29 07:00:00+00:00    3.66
2021-12-29 08:00:00+00:00    3.74
2021-12-29 09:00:00+00:00    3.73
2021-12-29 10:00:00+00:00    3.73
2021-12-29 11:00:00+00:00    3.74
2021-12-29 12:00:00+00:00    3.75
2021-12-29 13:00:00+00:00    3.75
2021-12-29 14:00:00+00:00    3.74
2021-12-29 15:00:00+00:00    3.74
2021-12-30 06:00:00+00:00    3.75
2021-12-30 07:00:00+00:00    3.75
2021-12-30 08:00:00+00:00    3.76
2021-12-30 09:00:00+00:00    3.75
2021-12-30 10:00:00+00:00    3.77
2021-12-30 11:00:00+00:00    3.74
2021-12-30 12:00:00+00:00    3.74
2021-12-30 13:00:00+00:00    3.75
2021-12-30 14:00:00+00:00    3.70
2021-12-30 15:00:00+00:00    3.71
2021-12-31 06:00:00+00:00    3.70
2021-12-31 07:00:00+00:00    3.69
2021-12-31 08:00:00+00:00    3.71
2021-12-31 09:00:00+00:00    3.69
2021-12-31 10:00:00+00:00    3.69
2021-12-31 11:00:00+00:00    3.67
2021-12-31 12:00:00+00:00    3.67
2021-12-31 13:00:00+00:00    3.65
2021-12-31 14:00:00+00:00    3.63
2021-12-31 15:00:00+00:00    3.68
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    14.75
2024-01-10 10:00:00+00:00    14.73
2024-01-10 10:30:00+00:00    14.77
2024-01-10 11:00:00+00:00    14.77
2024-01-10 11:30:00+00:00    14.76
2024-01-10 12:00:00+00:00    14.62
2024-01-10 12:30:00+00:00    14.71
2024-01-10 13:00:00+00:00    14.85
2024-01-10 13:30:00+00:00    14.87
2024-01-10 14:00:00+00:00    14.86
2024-01-10 15:00:00+00:00    14.81
2024-01-11 06:00:00+00:00    14.88
2024-01-11 06:30:00+00:00    14.98
2024-01-11 07:00:00+00:00    14.82
2024-01-11 07:30:00+00:00    14.73
2024-01-11 08:00:00+00:00    14.72
2024-01-11 08:30:00+00:00    14.75
2024-01-11 09:00:00+00:00    14.74
2024-01-11 09:30:00+00:00    14.73
2024-01-11 10:00:00+00:00    14.67
2024-01-11 10:30:00+00:00    14.73
2024-01-11 11:00:00+00:00    14.70
2024-01-11 11:30:00+00:00    14.66
2024-01-11 12:00:00+00:00    14.55
2024-01-11 12:30:00+00:00    14.67
2024-01-11 13:00:00+00:00    14.61
2024-01-11 13:30:00+00:00    14.63
2024-01-11 14:00:00+00:00    14.68
2024-01-11 14:30:00+00:00    14.60
2024-01-11 15:00:00+00:00    14.60
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    1.085000e-01
2018-01-02 11:00:00+03:00    7.880000e-02
2018-01-02 12:00:00+03:00    0.000000e+00
2018-01-02 13:00:00+03:00    9.900000e-03
2018-01-02 14:00:00+03:00    3.940000e-02
                                 ...     
2024-01-11 13:00:00+00:00   -6.000008e-02
2024-01-11 13:30:00+00:00    2.000011e-02
2024-01-11 14:00:00+00:00    4.999989e-02
2024-01-11 14:30:00+00:00   -7.999962e-02
2024-01-11 15:00:00+00:00   -3.814697e-07
Name: price, Length: 19222, dtype: float64
ADF Statistic: -19.272336007438795
p-value: 0.0
Critical Values: {'1%': -3.4306909715527216, '5%': -2.8616906970881657, '10%': -2.5668502120039105}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19223
Model:                ARIMA(30, 1, 1)   Log Likelihood               24301.619
Date:                Thu, 11 Jan 2024   AIC                         -48539.238
Time:                        22:27:25   BIC                         -48287.596
Sample:                             0   HQIC                        -48456.750
                              - 19223                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.0305      0.291     -0.105      0.917      -0.602       0.541
ar.L2          0.0173      0.018      0.976      0.329      -0.017       0.052
ar.L3          0.0131      0.006      2.154      0.031       0.001       0.025
ar.L4          0.0106      0.006      1.802      0.072      -0.001       0.022
ar.L5          0.0138      0.005      2.521      0.012       0.003       0.025
ar.L6          0.0144      0.006      2.228      0.026       0.002       0.027
ar.L7         -0.0028      0.007     -0.397      0.691      -0.017       0.011
ar.L8         -0.0033      0.005     -0.608      0.543      -0.014       0.007
ar.L9          0.0002      0.005      0.035      0.972      -0.010       0.011
ar.L10         0.0019      0.006      0.348      0.728      -0.009       0.013
ar.L11         0.0030      0.005      0.618      0.537      -0.006       0.012
ar.L12        -0.0035      0.005     -0.670      0.503      -0.014       0.007
ar.L13        -0.0108      0.005     -2.248      0.025      -0.020      -0.001
ar.L14         0.0145      0.005      2.707      0.007       0.004       0.025
ar.L15         0.0145      0.007      2.199      0.028       0.002       0.027
ar.L16         0.0109      0.005      2.130      0.033       0.001       0.021
ar.L17        -0.0096      0.006     -1.660      0.097      -0.021       0.002
ar.L18         0.0156      0.003      4.714      0.000       0.009       0.022
ar.L19         0.0606      0.005     11.816      0.000       0.051       0.071
ar.L20         0.0125      0.018      0.683      0.495      -0.023       0.048
ar.L21        -0.0247      0.006     -4.445      0.000      -0.036      -0.014
ar.L22         0.0032      0.008      0.378      0.705      -0.013       0.020
ar.L23        -0.0126      0.005     -2.770      0.006      -0.022      -0.004
ar.L24         0.0065      0.007      0.942      0.346      -0.007       0.020
ar.L25         0.0259      0.005      5.593      0.000       0.017       0.035
ar.L26         0.0040      0.010      0.419      0.675      -0.015       0.023
ar.L27        -0.0030      0.006     -0.468      0.640      -0.016       0.010
ar.L28         0.0035      0.005      0.646      0.519      -0.007       0.014
ar.L29        -0.0029      0.005     -0.576      0.565      -0.013       0.007
ar.L30         0.0169      0.005      3.170      0.002       0.006       0.027
ma.L1         -0.0294      0.292     -0.101      0.920      -0.601       0.542
sigma2         0.0047   1.02e-05    459.726      0.000       0.005       0.005
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           3646482.24
Prob(Q):                              1.00   Prob(JB):                         0.00
Heteroskedasticity (H):               7.81   Skew:                             0.70
Prob(H) (two-sided):                  0.00   Kurtosis:                        70.46
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223    14.600949
19224    14.608956
19225    14.602585
19226    14.591470
19227    14.592539
19228    14.592248
19229    14.598370
19230    14.597028
19231    14.590778
19232    14.589439
Name: predicted_mean, dtype: float64
       lower price  upper price
19223    14.467005    14.734893
19224    14.425117    14.792795
19225    14.378299    14.826870
19226    14.332224    14.850717
19227    14.301922    14.883155
19228    14.272591    14.911904
19229    14.251394    14.945346
19230    14.224894    14.969161
19231    14.195175    14.986380
19232    14.171644    15.007234

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

                           price short_name
timestamp                                  
2021-12-27 09:00:00+03:00  24.94      VESTL
2021-12-27 10:00:00+03:00  25.44      VESTL
2021-12-27 11:00:00+03:00  25.16      VESTL
2021-12-27 12:00:00+03:00  24.98      VESTL
2021-12-27 13:00:00+03:00  25.08      VESTL
...                          ...        ...
2024-01-11 14:00:00+03:00  48.16      VESTL
2024-01-11 15:00:00+03:00  47.92      VESTL
2024-01-11 16:00:00+03:00  47.90      VESTL
2024-01-11 17:00:00+03:00  48.20      VESTL
2024-01-11 18:00:00+03:00  48.38      VESTL

[5104 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    24.94
2021-12-27 07:00:00+00:00    25.44
2021-12-27 08:00:00+00:00    25.16
2021-12-27 09:00:00+00:00    24.98
2021-12-27 10:00:00+00:00    25.08
2021-12-27 11:00:00+00:00    24.68
2021-12-27 12:00:00+00:00    24.62
2021-12-27 13:00:00+00:00    24.70
2021-12-27 14:00:00+00:00    24.72
2021-12-27 15:00:00+00:00    24.68
2021-12-28 06:00:00+00:00    24.84
2021-12-28 07:00:00+00:00    24.74
2021-12-28 08:00:00+00:00    24.50
2021-12-28 09:00:00+00:00    24.42
2021-12-28 10:00:00+00:00    24.32
2021-12-28 11:00:00+00:00    24.26
2021-12-28 12:00:00+00:00    24.26
2021-12-28 13:00:00+00:00    24.10
2021-12-28 14:00:00+00:00    23.84
2021-12-28 15:00:00+00:00    23.80
2021-12-29 06:00:00+00:00    23.88
2021-12-29 07:00:00+00:00    23.66
2021-12-29 08:00:00+00:00    24.08
2021-12-29 09:00:00+00:00    24.16
2021-12-29 10:00:00+00:00    24.16
2021-12-29 11:00:00+00:00    24.16
2021-12-29 12:00:00+00:00    24.32
2021-12-29 13:00:00+00:00    24.36
2021-12-29 14:00:00+00:00    24.32
2021-12-29 15:00:00+00:00    24.28
2021-12-30 06:00:00+00:00    24.68
2021-12-30 07:00:00+00:00    24.38
2021-12-30 08:00:00+00:00    24.58
2021-12-30 09:00:00+00:00    24.48
2021-12-30 10:00:00+00:00    24.46
2021-12-30 11:00:00+00:00    24.18
2021-12-30 12:00:00+00:00    24.20
2021-12-30 13:00:00+00:00    24.18
2021-12-30 14:00:00+00:00    24.18
2021-12-30 15:00:00+00:00    24.10
2021-12-31 06:00:00+00:00    24.14
2021-12-31 07:00:00+00:00    24.04
2021-12-31 08:00:00+00:00    24.26
2021-12-31 09:00:00+00:00    24.40
2021-12-31 10:00:00+00:00    24.40
2021-12-31 11:00:00+00:00    24.50
2021-12-31 12:00:00+00:00    24.48
2021-12-31 13:00:00+00:00    24.66
2021-12-31 14:00:00+00:00    24.94
2021-12-31 15:00:00+00:00    25.04
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    47.680000
2024-01-10 10:00:00+00:00    47.680000
2024-01-10 10:30:00+00:00    47.639999
2024-01-10 11:00:00+00:00    47.640000
2024-01-10 11:30:00+00:00    47.680000
2024-01-10 12:00:00+00:00    47.620000
2024-01-10 12:30:00+00:00    47.840000
2024-01-10 13:00:00+00:00    48.620000
2024-01-10 13:30:00+00:00    48.459999
2024-01-10 14:00:00+00:00    48.680000
2024-01-10 15:00:00+00:00    48.640000
2024-01-11 06:00:00+00:00    48.640000
2024-01-11 06:30:00+00:00    48.480000
2024-01-11 07:00:00+00:00    48.400000
2024-01-11 07:30:00+00:00    48.279999
2024-01-11 08:00:00+00:00    48.240000
2024-01-11 08:30:00+00:00    48.380001
2024-01-11 09:00:00+00:00    48.320000
2024-01-11 09:30:00+00:00    48.259998
2024-01-11 10:00:00+00:00    48.240000
2024-01-11 10:30:00+00:00    48.180000
2024-01-11 11:00:00+00:00    48.160000
2024-01-11 11:30:00+00:00    48.200001
2024-01-11 12:00:00+00:00    47.920000
2024-01-11 12:30:00+00:00    48.220001
2024-01-11 13:00:00+00:00    47.900000
2024-01-11 13:30:00+00:00    47.939999
2024-01-11 14:00:00+00:00    48.200000
2024-01-11 14:30:00+00:00    48.380001
2024-01-11 15:00:00+00:00    48.380000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    0.173300
2018-01-02 11:00:00+03:00    0.315000
2018-01-02 12:00:00+03:00    0.039300
2018-01-02 13:00:00+03:00    0.000000
2018-01-02 14:00:00+03:00    0.322900
                               ...   
2024-01-11 13:00:00+00:00   -0.320001
2024-01-11 13:30:00+00:00    0.039999
2024-01-11 14:00:00+00:00    0.260001
2024-01-11 14:30:00+00:00    0.180001
2024-01-11 15:00:00+00:00   -0.000001
Name: price, Length: 19222, dtype: float64
ADF Statistic: -23.075424884400462
p-value: 0.0
Critical Values: {'1%': -3.430690918224345, '5%': -2.861690673520306, '10%': -2.566850199459138}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19223
Model:                ARIMA(30, 1, 1)   Log Likelihood               -3308.365
Date:                Thu, 11 Jan 2024   AIC                           6680.731
Time:                        22:27:57   BIC                           6932.373
Sample:                             0   HQIC                          6763.219
                              - 19223                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.0035      1.277     -0.003      0.998      -2.507       2.500
ar.L2         -0.0384      0.010     -3.931      0.000      -0.058      -0.019
ar.L3          0.0003      0.049      0.007      0.994      -0.096       0.097
ar.L4          0.0007      0.004      0.194      0.847      -0.007       0.008
ar.L5          0.0010      0.004      0.236      0.813      -0.007       0.009
ar.L6        9.64e-05      0.004      0.022      0.982      -0.008       0.009
ar.L7          0.0111      0.004      2.683      0.007       0.003       0.019
ar.L8          0.0102      0.015      0.680      0.496      -0.019       0.040
ar.L9          0.0291      0.014      2.139      0.032       0.002       0.056
ar.L10         0.0030      0.037      0.080      0.936      -0.070       0.076
ar.L11         0.0073      0.005      1.342      0.179      -0.003       0.018
ar.L12        -0.0169      0.010     -1.618      0.106      -0.037       0.004
ar.L13         0.0232      0.022      1.041      0.298      -0.021       0.067
ar.L14         0.0200      0.030      0.670      0.503      -0.038       0.078
ar.L15         0.0106      0.026      0.411      0.681      -0.040       0.061
ar.L16        -0.0107      0.014     -0.744      0.457      -0.039       0.017
ar.L17         0.0088      0.014      0.624      0.533      -0.019       0.037
ar.L18         0.0141      0.012      1.216      0.224      -0.009       0.037
ar.L19         0.0045      0.018      0.246      0.806      -0.032       0.041
ar.L20        -0.0182      0.007     -2.603      0.009      -0.032      -0.004
ar.L21        -0.0013      0.024     -0.056      0.955      -0.047       0.045
ar.L22        -0.0050      0.004     -1.195      0.232      -0.013       0.003
ar.L23        -0.0080      0.008     -1.022      0.307      -0.023       0.007
ar.L24        -0.0138      0.011     -1.263      0.207      -0.035       0.008
ar.L25        -0.0096      0.018     -0.530      0.596      -0.045       0.026
ar.L26        -0.0106      0.013     -0.828      0.408      -0.036       0.015
ar.L27        -0.0067      0.014     -0.466      0.641      -0.035       0.021
ar.L28        -0.0178      0.009     -1.885      0.059      -0.036       0.001
ar.L29         0.0012      0.023      0.049      0.961      -0.045       0.047
ar.L30         0.0034      0.004      0.856      0.392      -0.004       0.011
ma.L1         -0.0038      1.277     -0.003      0.998      -2.506       2.499
sigma2         0.0826      0.000    381.870      0.000       0.082       0.083
===================================================================================
Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):           2142776.40
Prob(Q):                              0.99   Prob(JB):                         0.00
Heteroskedasticity (H):              25.80   Skew:                             1.38
Prob(H) (two-sided):                  0.00   Kurtosis:                        54.65
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19223    48.361908
19224    48.347745
19225    48.328307
19226    48.324971
19227    48.312104
19228    48.311258
19229    48.311661
19230    48.327682
19231    48.328404
19232    48.328275
Name: predicted_mean, dtype: float64
       lower price  upper price
19223    47.798593    48.925222
19224    47.554004    49.141487
19225    47.369654    49.286960
19226    47.225670    49.424273
19227    47.087667    49.536541
19228    46.973126    49.649390
19229    46.868770    49.754552
19230    46.784946    49.870417
19231    46.689995    49.966814
19232    46.594361    50.062188

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(

                             price short_name
timestamp                                    
2021-12-27 09:00:00+03:00   3.1711      YKBNK
2021-12-27 10:00:00+03:00   3.2338      YKBNK
2021-12-27 11:00:00+03:00   3.2517      YKBNK
2021-12-27 12:00:00+03:00   3.2517      YKBNK
2021-12-27 13:00:00+03:00   3.2517      YKBNK
...                            ...        ...
2024-01-11 14:00:00+03:00  23.4800      YKBNK
2024-01-11 15:00:00+03:00  23.3200      YKBNK
2024-01-11 16:00:00+03:00  23.3400      YKBNK
2024-01-11 17:00:00+03:00  23.3000      YKBNK
2024-01-11 18:00:00+03:00  23.3000      YKBNK

[5104 rows x 2 columns]
[*********************100%%**********************]  1 of 1 completed
2021-12-27 06:00:00+00:00    3.1711
2021-12-27 07:00:00+00:00    3.2338
2021-12-27 08:00:00+00:00    3.2517
2021-12-27 09:00:00+00:00    3.2517
2021-12-27 10:00:00+00:00    3.2517
2021-12-27 11:00:00+00:00    3.2249
2021-12-27 12:00:00+00:00    3.2697
2021-12-27 13:00:00+00:00    3.2965
2021-12-27 14:00:00+00:00    3.2786
2021-12-27 15:00:00+00:00    3.2786
2021-12-28 06:00:00+00:00    3.2607
2021-12-28 07:00:00+00:00    3.1711
2021-12-28 08:00:00+00:00    3.1532
2021-12-28 09:00:00+00:00    3.1532
2021-12-28 10:00:00+00:00    3.1443
2021-12-28 11:00:00+00:00    3.1532
2021-12-28 12:00:00+00:00    3.1532
2021-12-28 13:00:00+00:00    3.1353
2021-12-28 14:00:00+00:00    3.0816
2021-12-28 15:00:00+00:00    3.0816
2021-12-29 06:00:00+00:00    3.0726
2021-12-29 07:00:00+00:00    3.0547
2021-12-29 08:00:00+00:00    3.0994
2021-12-29 09:00:00+00:00    3.0905
2021-12-29 10:00:00+00:00    3.0816
2021-12-29 11:00:00+00:00    3.0905
2021-12-29 12:00:00+00:00    3.0994
2021-12-29 13:00:00+00:00    3.0994
2021-12-29 14:00:00+00:00    3.0994
2021-12-29 15:00:00+00:00    3.0994
2021-12-30 06:00:00+00:00    3.1263
2021-12-30 07:00:00+00:00    3.1532
2021-12-30 08:00:00+00:00    3.1711
2021-12-30 09:00:00+00:00    3.1711
2021-12-30 10:00:00+00:00    3.1801
2021-12-30 11:00:00+00:00    3.1711
2021-12-30 12:00:00+00:00    3.1711
2021-12-30 13:00:00+00:00    3.1622
2021-12-30 14:00:00+00:00    3.1263
2021-12-30 15:00:00+00:00    3.1174
2021-12-31 06:00:00+00:00    3.0994
2021-12-31 07:00:00+00:00    3.0905
2021-12-31 08:00:00+00:00    3.0994
2021-12-31 09:00:00+00:00    3.1084
2021-12-31 10:00:00+00:00    3.0636
2021-12-31 11:00:00+00:00    3.0636
2021-12-31 12:00:00+00:00    3.0457
2021-12-31 13:00:00+00:00    3.0367
2021-12-31 14:00:00+00:00    3.0189
2021-12-31 15:00:00+00:00    3.0278
Name: price, dtype: float64
2024-01-10 09:30:00+00:00    22.260000
2024-01-10 10:00:00+00:00    22.340000
2024-01-10 10:30:00+00:00    22.480000
2024-01-10 11:00:00+00:00    22.420000
2024-01-10 11:30:00+00:00    22.480000
2024-01-10 12:00:00+00:00    22.360000
2024-01-10 12:30:00+00:00    22.540001
2024-01-10 13:00:00+00:00    23.080000
2024-01-10 13:30:00+00:00    23.320000
2024-01-10 14:00:00+00:00    23.540000
2024-01-10 15:00:00+00:00    23.560000
2024-01-11 06:00:00+00:00    23.680000
2024-01-11 06:30:00+00:00    23.480000
2024-01-11 07:00:00+00:00    23.300000
2024-01-11 07:30:00+00:00    23.420000
2024-01-11 08:00:00+00:00    23.480000
2024-01-11 08:30:00+00:00    23.500000
2024-01-11 09:00:00+00:00    23.560000
2024-01-11 09:30:00+00:00    23.520000
2024-01-11 10:00:00+00:00    23.460000
2024-01-11 10:30:00+00:00    23.520000
2024-01-11 11:00:00+00:00    23.480000
2024-01-11 11:30:00+00:00    23.440001
2024-01-11 12:00:00+00:00    23.320000
2024-01-11 12:30:00+00:00    23.500000
2024-01-11 13:00:00+00:00    23.340000
2024-01-11 13:30:00+00:00    23.340000
2024-01-11 14:00:00+00:00    23.300000
2024-01-11 14:30:00+00:00    23.299999
2024-01-11 15:00:00+00:00    23.300000
Name: price, dtype: float64
2018-01-02 10:00:00+03:00    1.690000e-02
2018-01-02 11:00:00+03:00    1.690000e-02
2018-01-02 12:00:00+03:00    0.000000e+00
2018-01-02 13:00:00+03:00    0.000000e+00
2018-01-02 14:00:00+03:00    1.670000e-02
                                 ...     
2024-01-11 13:00:00+00:00   -1.600000e-01
2024-01-11 13:30:00+00:00    1.525879e-07
2024-01-11 14:00:00+00:00   -4.000015e-02
2024-01-11 14:30:00+00:00   -7.629395e-07
2024-01-11 15:00:00+00:00    7.629395e-07
Name: price, Length: 19221, dtype: float64
ADF Statistic: -21.08167856949447
p-value: 0.0
Critical Values: {'1%': -3.4306910782595423, '5%': -2.8616907442460104, '10%': -2.5668502371052324}

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.
  self._init_dates(dates, freq)
C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

                               SARIMAX Results                                
==============================================================================
Dep. Variable:                  price   No. Observations:                19222
Model:                ARIMA(30, 1, 1)   Log Likelihood               14842.044
Date:                Thu, 11 Jan 2024   AIC                         -29620.088
Time:                        22:30:36   BIC                         -29368.448
Sample:                             0   HQIC                        -29537.601
                              - 19222                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.4303      0.118     -3.640      0.000      -0.662      -0.199
ar.L2          0.2249      0.088      2.557      0.011       0.053       0.397
ar.L3         -0.0466      0.005     -9.454      0.000      -0.056      -0.037
ar.L4          0.0165      0.008      2.072      0.038       0.001       0.032
ar.L5         -0.0389      0.005     -7.184      0.000      -0.050      -0.028
ar.L6          0.0342      0.007      4.703      0.000       0.020       0.048
ar.L7         -0.0216      0.007     -3.177      0.001      -0.035      -0.008
ar.L8          0.0283      0.007      4.094      0.000       0.015       0.042
ar.L9         -0.0334      0.008     -4.408      0.000      -0.048      -0.019
ar.L10         0.0391      0.008      5.184      0.000       0.024       0.054
ar.L11         0.0086      0.007      1.215      0.224      -0.005       0.023
ar.L12        -0.0304      0.007     -4.630      0.000      -0.043      -0.018
ar.L13        -0.0096      0.006     -1.647      0.100      -0.021       0.002
ar.L14        -0.0278      0.005     -5.093      0.000      -0.039      -0.017
ar.L15         0.0008      0.006      0.124      0.901      -0.012       0.013
ar.L16        -0.1492      0.004    -37.889      0.000      -0.157      -0.142
ar.L17        -0.0402      0.018     -2.183      0.029      -0.076      -0.004
ar.L18         0.3794      0.011     35.270      0.000       0.358       0.400
ar.L19         0.2830      0.042      6.801      0.000       0.201       0.365
ar.L20        -0.2598      0.046     -5.591      0.000      -0.351      -0.169
ar.L21        -0.0879      0.017     -5.188      0.000      -0.121      -0.055
ar.L22         0.0040      0.016      0.244      0.807      -0.028       0.036
ar.L23        -0.0482      0.007     -6.829      0.000      -0.062      -0.034
ar.L24         0.0126      0.009      1.362      0.173      -0.006       0.031
ar.L25        -0.0375      0.006     -5.967      0.000      -0.050      -0.025
ar.L26         0.0345      0.008      4.383      0.000       0.019       0.050
ar.L27        -0.0138      0.007     -1.886      0.059      -0.028       0.001
ar.L28         0.0190      0.007      2.766      0.006       0.006       0.032
ar.L29        -0.0649      0.007     -9.387      0.000      -0.078      -0.051
ar.L30         0.0676      0.010      6.740      0.000       0.048       0.087
ma.L1         -0.3107      0.118     -2.628      0.009      -0.542      -0.079
sigma2         0.0125   5.24e-05    238.275      0.000       0.012       0.013
===================================================================================
Ljung-Box (L1) (Q):                   0.11   Jarque-Bera (JB):            212572.43
Prob(Q):                              0.74   Prob(JB):                         0.00
Heteroskedasticity (H):              74.35   Skew:                            -0.35
Prob(H) (two-sided):                  0.00   Kurtosis:                        19.28
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
19222    23.207419
19223    23.042690
19224    23.130090
19225    23.155577
19226    23.196759
19227    23.198195
19228    23.173311
19229    23.161470
19230    23.178304
19231    23.219559
Name: predicted_mean, dtype: float64
       lower price  upper price
19222    22.988375    23.426463
19223    22.816413    23.268966
19224    22.843527    23.416653
19225    22.858615    23.452539
19226    22.860389    23.533128
19227    22.851311    23.545079
19228    22.793537    23.553086
19229    22.771669    23.551270
19230    22.758972    23.597636
19231    22.791612    23.647507

C:\Users\DELL\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:834: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.
  return get_prediction_index(
In [6]:
submissions
Out[6]:
{'THYAO': array([246.72844711, 246.78783408, 246.9015646 , 247.04747162,
        247.21927253, 247.20683269, 247.09158936, 247.06970646,
        246.88399065, 246.69084918]),
 'AKBNK': array([41.21498555, 41.00335625, 40.9756319 , 41.09456064, 41.06168266,
        41.15711143, 41.0643389 , 41.14454865, 41.06272159, 41.05295957]),
 'ARCLK': array([129.63910393, 129.56538056, 129.32843629, 129.40164336,
        129.31511734, 129.60642998, 129.43727861, 129.51283904,
        129.41582614, 129.59837851]),
 'ASELS': array([46.88617574, 46.84543606, 46.85122809, 46.816103  , 46.82289484,
        46.78491206, 46.74780343, 46.71490225, 46.66539834, 46.63860407]),
 'BIMAS': array([328.78062314, 328.66001175, 328.22562358, 328.25766629,
        328.29920129, 328.80807997, 328.01343952, 328.24228676,
        328.07770633, 328.29868007]),
 'DOHOL': array([11.99364808, 11.9893169 , 11.99364603, 11.98947157, 11.99346285,
        11.98965457, 11.99328641, 11.98982327, 11.99312545, 11.98997677]),
 'EKGYO': array([8.1760892 , 8.16861706, 8.17628254, 8.16872132, 8.17610686,
        8.16891115, 8.17591729, 8.16909689, 8.17573618, 8.16927327]),
 'EREGL': array([44.03397781, 43.65898765, 43.5219273 , 43.60978512, 43.59964088,
        43.5439619 , 43.45007406, 43.3357611 , 43.28272702, 43.19063575]),
 'FROTO': array([779.87342804, 778.04053532, 775.83986295, 777.67485481,
        777.12717702, 779.22275821, 778.1245483 , 779.73898544,
        777.84203238, 778.29933116]),
 'GUBRF': array([141.14868834, 140.66977052, 140.29267173, 140.02522546,
        139.92420721, 139.84095072, 139.5790833 , 139.32688028,
        139.12621956, 138.88119294]),
 'GARAN': array([66.41754158, 66.53585287, 65.82444262, 66.19984193, 65.98487337,
        66.03820567, 65.97803918, 65.81657186, 65.92891955, 66.03950152]),
 'KRDMD': array([26.00348312, 25.9451383 , 25.94373154, 25.97031833, 26.0845764 ,
        26.06383759, 26.11426207, 26.11315538, 26.14346082, 26.21395149]),
 'KCHOL': array([153.66963405, 153.73156044, 154.24359365, 154.41723085,
        155.15239297, 155.51485854, 155.7242077 , 155.9434715 ,
        156.05799333, 156.3638059 ]),
 'KOZAL': array([20.18335777, 20.10946599, 20.19596036, 20.14809994, 20.16828438,
        20.07120736, 20.12323704, 20.10293381, 20.10383275, 20.11297086]),
 'KOZAA': array([43.58409714, 43.63007384, 43.66671951, 43.67640041, 43.68518527,
        43.74358314, 43.77852245, 43.85590848, 43.93533953, 43.96617439]),
 'PGSUS': array([711.68701056, 711.5365248 , 711.64407539, 711.85089627,
        712.53301439, 712.68061064, 712.85942809, 713.40377985,
        713.23125806, 713.06654395]),
 'PETKM': array([20.01677177, 19.99448174, 19.97336809, 19.94793367, 19.92779044,
        19.91660949, 19.90646131, 19.90145363, 19.88604725, 19.85763811]),
 'SAHOL': array([68.38458445, 68.2174538 , 68.18155277, 68.29138276, 68.47217024,
        68.47658219, 68.27603463, 68.22292766, 68.24880712, 68.40455454]),
 'SASA': array([35.07289163, 35.00749169, 34.98824863, 34.96465225, 34.9545911 ,
        34.96251762, 34.95880461, 34.95779422, 34.95283677, 34.94501868]),
 'SISE': array([47.75245292, 47.77226825, 47.71063486, 47.75187081, 47.74509235,
        47.80246512, 47.7562865 , 47.76060881, 47.74026177, 47.75606647]),
 'TAVHL': array([117.84888577, 117.88400361, 117.86808255, 117.89298502,
        117.90717193, 117.95666838, 117.96566047, 118.0220838 ,
        118.08621704, 118.02767048]),
 'TKFEN': array([37.57401907, 37.71791395, 37.57580652, 37.58368395, 37.58521004,
        37.57349133, 37.60297795, 37.52268025, 37.48154465, 37.56920221]),
 'TUPRS': array([140.17831703, 140.33654821, 140.13103461, 139.50768859,
        139.84930153, 140.15384346, 139.52881033, 139.4866555 ,
        139.48659775, 139.64620324]),
 'TTKOM': array([26.76106245, 26.6578966 , 26.67664835, 26.53237998, 26.56686002,
        26.50274109, 26.448061  , 26.37888021, 26.3160813 , 26.28693917]),
 'TCELL': array([60.84638811, 60.76452249, 60.82680552, 60.79867889, 60.8232561 ,
        60.85986885, 60.80905317, 60.83287196, 60.7649962 , 60.87972188]),
 'HALKB': array([13.02436095, 13.02755185, 13.02814561, 13.01921732, 13.01801652,
        13.0209602 , 13.02302678, 13.02065749, 13.01620852, 13.01810905]),
 'ISCTR': array([25.76553266, 25.57376328, 25.61390867, 25.64279202, 25.66735909,
        25.6504995 , 25.67051812, 25.70289366, 25.73571323, 25.72625894]),
 'VAKBN': array([14.60094897, 14.60895631, 14.60258455, 14.59147041, 14.59253866,
        14.59224751, 14.59837002, 14.59702767, 14.59077751, 14.58943921]),
 'VESTL': array([48.36190757, 48.34774533, 48.32830684, 48.32497134, 48.31210428,
        48.31125819, 48.31166112, 48.32768156, 48.32840434, 48.32827484]),
 'YKBNK': array([23.20741887, 23.04268983, 23.13008961, 23.15557701, 23.19675869,
        23.198195  , 23.17331142, 23.16146988, 23.17830398, 23.21955939])}
In [7]:
import numpy as np


submission = {key: value.tolist() for key, value in submissions.items()}

print(submission)

{'THYAO': [246.72844710723302, 246.78783407728295, 246.9015646038812, 247.04747162124343, 247.21927253115817, 247.206832691938, 247.09158936183306, 247.06970645523555, 246.8839906543494, 246.69084918318993], 'AKBNK': [41.21498555020162, 41.003356245551444, 40.97563189982208, 41.09456064284728, 41.061682660223795, 41.15711143390959, 41.06433890285226, 41.14454865391768, 41.062721588704555, 41.05295956678886], 'ARCLK': [129.63910392729005, 129.5653805588673, 129.32843629297253, 129.4016433612058, 129.31511733975594, 129.6064299831944, 129.43727860615024, 129.51283903684234, 129.4158261358366, 129.59837851217276], 'ASELS': [46.88617574472465, 46.84543605887873, 46.85122808676905, 46.816103004331715, 46.822894838354046, 46.7849120619001, 46.74780343144717, 46.714902254691836, 46.665398341571674, 46.63860407377028], 'BIMAS': [328.7806231443262, 328.660011754543, 328.22562358258256, 328.257666293802, 328.2992012866527, 328.80807996561924, 328.01343952038064, 328.2422867609498, 328.0777063340028, 328.29868006578994], 'DOHOL': [11.993648080732326, 11.989316897335875, 11.993646028255652, 11.98947156928321, 11.993462853579727, 11.989654570889067, 11.993286409521632, 11.989823268161206, 11.993125448107174, 11.989976771724155], 'EKGYO': [8.176089198026078, 8.168617058890318, 8.17628254176901, 8.16872131584169, 8.176106860757438, 8.168911151105908, 8.175917294484885, 8.169096891952474, 8.175736181456779, 8.169273270781389], 'EREGL': [44.033977808646775, 43.65898764706641, 43.52192729761015, 43.60978512412417, 43.59964088318589, 43.54396190254043, 43.450074056645256, 43.335761103158546, 43.282727016516255, 43.19063575180934], 'FROTO': [779.8734280437163, 778.040535324543, 775.8398629549137, 777.6748548062105, 777.1271770156923, 779.2227582076428, 778.1245482998045, 779.7389854447762, 777.8420323760793, 778.2993311606198], 'GUBRF': [141.14868833606002, 140.66977052373915, 140.2926717268744, 140.02522545858636, 139.9242072059186, 139.84095071897008, 139.57908329880217, 139.3268802803794, 139.1262195621227, 138.8811929435401], 'GARAN': [66.41754157833445, 66.53585286787546, 65.82444262019675, 66.19984193302288, 65.98487336967912, 66.03820566668699, 65.97803918002698, 65.81657186437555, 65.92891954947265, 66.03950152208415], 'KRDMD': [26.003483119174458, 25.945138297880394, 25.943731541433944, 25.97031833106211, 26.084576403334086, 26.063837593446028, 26.11426207202655, 26.11315538255608, 26.14346082223534, 26.21395149198643], 'KCHOL': [153.66963405401702, 153.7315604371095, 154.24359364903768, 154.41723084727934, 155.15239296621877, 155.51485854332546, 155.72420770380123, 155.9434715033698, 156.0579933349858, 156.36380589821755], 'KOZAL': [20.183357772837123, 20.10946598605728, 20.19596035657695, 20.148099942729207, 20.168284379075654, 20.07120735780075, 20.123237037077896, 20.10293381350078, 20.103832746979915, 20.112970859929025], 'KOZAA': [43.58409713509102, 43.63007384034846, 43.66671951126056, 43.67640040639069, 43.68518527393528, 43.74358313974144, 43.77852245417585, 43.85590848162289, 43.93533952847479, 43.96617438567008], 'PGSUS': [711.6870105562964, 711.536524799276, 711.6440753868644, 711.8508962692003, 712.5330143946644, 712.6806106413915, 712.8594280906352, 713.4037798471778, 713.2312580640764, 713.0665439470387], 'PETKM': [20.01677176893671, 19.994481740056173, 19.973368088184127, 19.947933665007817, 19.927790442035466, 19.91660948655672, 19.906461309311435, 19.901453628973528, 19.886047250272114, 19.85763811401552], 'SAHOL': [68.3845844480737, 68.21745379648303, 68.18155277127364, 68.29138276136075, 68.47217024429902, 68.4765821858676, 68.2760346251081, 68.22292766205862, 68.24880712057283, 68.40455454160326], 'SASA': [35.07289162969731, 35.0074916936396, 34.98824863216797, 34.96465224704032, 34.95459110419446, 34.96251761524504, 34.958804608720634, 34.9577942176785, 34.95283676890159, 34.945018677702876], 'SISE': [47.75245292108193, 47.77226824615632, 47.71063485953328, 47.75187080968883, 47.74509234582463, 47.8024651212386, 47.75628650037016, 47.76060881261082, 47.74026176660561, 47.75606647443745], 'TAVHL': [117.84888576546061, 117.88400360910454, 117.86808255475137, 117.8929850188489, 117.9071719277577, 117.95666838100395, 117.96566046535648, 118.02208379861436, 118.08621704416655, 118.02767048197585], 'TKFEN': [37.574019071922955, 37.71791395434733, 37.57580651720906, 37.583683953908455, 37.58521003562684, 37.573491327158486, 37.60297794877059, 37.52268024961215, 37.4815446545906, 37.569202208787225], 'TUPRS': [140.17831702571735, 140.33654821068933, 140.13103461456555, 139.5076885946764, 139.8493015287174, 140.15384345909465, 139.52881032602934, 139.48665549888972, 139.4865977452933, 139.64620323797956], 'TTKOM': [26.761062454570094, 26.6578965954013, 26.676648346093806, 26.5323799770588, 26.566860015557413, 26.502741090922882, 26.448060997741788, 26.37888020763176, 26.316081299524747, 26.28693916999035], 'TCELL': [60.846388107561275, 60.76452248634553, 60.82680551564222, 60.798678891385684, 60.82325609915799, 60.85986885423484, 60.809053169830264, 60.832871955881096, 60.764996201238155, 60.8797218816646], 'HALKB': [13.024360946385308, 13.027551854513327, 13.02814560651817, 13.019217323616965, 13.018016524704374, 13.020960203255427, 13.023026781385669, 13.020657489793118, 13.016208518193404, 13.018109052410637], 'ISCTR': [25.765532658963668, 25.57376328405084, 25.613908668205458, 25.642792023906694, 25.66735909487472, 25.650499499627152, 25.67051811776372, 25.70289365577941, 25.735713232556755, 25.726258942402637], 'VAKBN': [14.600948968404444, 14.608956306749047, 14.602584554909672, 14.591470406399264, 14.592538661014462, 14.592247509005302, 14.598370018737963, 14.59702766599865, 14.590777511610774, 14.589439209184151], 'VESTL': [48.36190757277486, 48.347745325123654, 48.328306841442604, 48.32497134030203, 48.31210428274845, 48.31125818597144, 48.3116611183435, 48.32768156441343, 48.32840433503494, 48.32827483820829], 'YKBNK': [23.207418865505897, 23.04268982835163, 23.130089607032154, 23.15557701265702, 23.196758689846163, 23.198194995599696, 23.17331142323614, 23.161469881125882, 23.17830398151349, 23.21955938808481]}
In [185]:
len(submission)
Out[185]:
30
In [186]:
length=0
for pred in submission.values():
    length += len(pred)
length
Out[186]:
300
In [187]:
length=0
for pred in submission.values():
    print(len(pred))

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